Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis

Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zheng, Tingyi [verfasserIn] Ge, Huibin [verfasserIn] Li, Jiayi [verfasserIn] Wang, Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Unsupervised multi-view representation learning Proximity guided dynamic routing Latent specific characteristic Discrimination representation Generalized canonical correlation analysis

Übergeordnetes Werk:	Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 51(2020), 1 vom: 10. Aug., Seite 248-264
Übergeordnetes Werk:	volume:51 ; year:2020 ; number:1 ; day:10 ; month:08 ; pages:248-264

Links:	Volltext

DOI / URN:	10.1007/s10489-020-01821-1

Katalog-ID:	SPR042532132

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520			\|a Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL.
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author_variant	t z tz h g hg j l jl l w lw
matchkey_str	article:15737497:2020----::nuevsdutverpeettolannwtpoiiyuddersnainngnrl
hierarchy_sort_str	2020
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publishDate	2020
allfields	10.1007/s10489-020-01821-1 doi (DE-627)SPR042532132 (DE-599)SPRs10489-020-01821-1-e (SPR)s10489-020-01821-1-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Zheng, Tingyi verfasserin aut Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL. Unsupervised multi-view representation learning (dpeaa)DE-He213 Proximity guided dynamic routing (dpeaa)DE-He213 Latent specific characteristic (dpeaa)DE-He213 Discrimination representation (dpeaa)DE-He213 Generalized canonical correlation analysis (dpeaa)DE-He213 Ge, Huibin verfasserin aut Li, Jiayi verfasserin aut Wang, Li verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 51(2020), 1 vom: 10. Aug., Seite 248-264 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:51 year:2020 number:1 day:10 month:08 pages:248-264 https://dx.doi.org/10.1007/s10489-020-01821-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 51 2020 1 10 08 248-264
spelling	10.1007/s10489-020-01821-1 doi (DE-627)SPR042532132 (DE-599)SPRs10489-020-01821-1-e (SPR)s10489-020-01821-1-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Zheng, Tingyi verfasserin aut Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL. Unsupervised multi-view representation learning (dpeaa)DE-He213 Proximity guided dynamic routing (dpeaa)DE-He213 Latent specific characteristic (dpeaa)DE-He213 Discrimination representation (dpeaa)DE-He213 Generalized canonical correlation analysis (dpeaa)DE-He213 Ge, Huibin verfasserin aut Li, Jiayi verfasserin aut Wang, Li verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 51(2020), 1 vom: 10. Aug., Seite 248-264 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:51 year:2020 number:1 day:10 month:08 pages:248-264 https://dx.doi.org/10.1007/s10489-020-01821-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 51 2020 1 10 08 248-264
allfields_unstemmed	10.1007/s10489-020-01821-1 doi (DE-627)SPR042532132 (DE-599)SPRs10489-020-01821-1-e (SPR)s10489-020-01821-1-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Zheng, Tingyi verfasserin aut Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL. Unsupervised multi-view representation learning (dpeaa)DE-He213 Proximity guided dynamic routing (dpeaa)DE-He213 Latent specific characteristic (dpeaa)DE-He213 Discrimination representation (dpeaa)DE-He213 Generalized canonical correlation analysis (dpeaa)DE-He213 Ge, Huibin verfasserin aut Li, Jiayi verfasserin aut Wang, Li verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 51(2020), 1 vom: 10. Aug., Seite 248-264 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:51 year:2020 number:1 day:10 month:08 pages:248-264 https://dx.doi.org/10.1007/s10489-020-01821-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 51 2020 1 10 08 248-264
allfieldsGer	10.1007/s10489-020-01821-1 doi (DE-627)SPR042532132 (DE-599)SPRs10489-020-01821-1-e (SPR)s10489-020-01821-1-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Zheng, Tingyi verfasserin aut Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL. Unsupervised multi-view representation learning (dpeaa)DE-He213 Proximity guided dynamic routing (dpeaa)DE-He213 Latent specific characteristic (dpeaa)DE-He213 Discrimination representation (dpeaa)DE-He213 Generalized canonical correlation analysis (dpeaa)DE-He213 Ge, Huibin verfasserin aut Li, Jiayi verfasserin aut Wang, Li verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 51(2020), 1 vom: 10. Aug., Seite 248-264 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:51 year:2020 number:1 day:10 month:08 pages:248-264 https://dx.doi.org/10.1007/s10489-020-01821-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 51 2020 1 10 08 248-264
allfieldsSound	10.1007/s10489-020-01821-1 doi (DE-627)SPR042532132 (DE-599)SPRs10489-020-01821-1-e (SPR)s10489-020-01821-1-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Zheng, Tingyi verfasserin aut Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL. Unsupervised multi-view representation learning (dpeaa)DE-He213 Proximity guided dynamic routing (dpeaa)DE-He213 Latent specific characteristic (dpeaa)DE-He213 Discrimination representation (dpeaa)DE-He213 Generalized canonical correlation analysis (dpeaa)DE-He213 Ge, Huibin verfasserin aut Li, Jiayi verfasserin aut Wang, Li verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 51(2020), 1 vom: 10. Aug., Seite 248-264 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:51 year:2020 number:1 day:10 month:08 pages:248-264 https://dx.doi.org/10.1007/s10489-020-01821-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 51 2020 1 10 08 248-264
language	English
source	Enthalten in Applied intelligence 51(2020), 1 vom: 10. Aug., Seite 248-264 volume:51 year:2020 number:1 day:10 month:08 pages:248-264
sourceStr	Enthalten in Applied intelligence 51(2020), 1 vom: 10. Aug., Seite 248-264 volume:51 year:2020 number:1 day:10 month:08 pages:248-264
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Unsupervised multi-view representation learning Proximity guided dynamic routing Latent specific characteristic Discrimination representation Generalized canonical correlation analysis
dewey-raw	004
isfreeaccess_bool	false
container_title	Applied intelligence
authorswithroles_txt_mv	Zheng, Tingyi @@aut@@ Ge, Huibin @@aut@@ Li, Jiayi @@aut@@ Wang, Li @@aut@@
publishDateDaySort_date	2020-08-10T00:00:00Z
hierarchy_top_id	271180919
dewey-sort	14
id	SPR042532132
language_de	englisch
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author	Zheng, Tingyi
spellingShingle	Zheng, Tingyi ddc 004 bkl 54.72 bkl 30.20 misc Unsupervised multi-view representation learning misc Proximity guided dynamic routing misc Latent specific characteristic misc Discrimination representation misc Generalized canonical correlation analysis Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis
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topic_title	004 ASE 54.72 bkl 30.20 bkl Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis Unsupervised multi-view representation learning (dpeaa)DE-He213 Proximity guided dynamic routing (dpeaa)DE-He213 Latent specific characteristic (dpeaa)DE-He213 Discrimination representation (dpeaa)DE-He213 Generalized canonical correlation analysis (dpeaa)DE-He213
topic	ddc 004 bkl 54.72 bkl 30.20 misc Unsupervised multi-view representation learning misc Proximity guided dynamic routing misc Latent specific characteristic misc Discrimination representation misc Generalized canonical correlation analysis
topic_unstemmed	ddc 004 bkl 54.72 bkl 30.20 misc Unsupervised multi-view representation learning misc Proximity guided dynamic routing misc Latent specific characteristic misc Discrimination representation misc Generalized canonical correlation analysis
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title	Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis
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title_full	Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis
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author_browse	Zheng, Tingyi Ge, Huibin Li, Jiayi Wang, Li
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title_sort	unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis
title_auth	Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis
abstract	Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL.
abstractGer	Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL.
abstract_unstemmed	Abstract Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL.
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container_issue	1
title_short	Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis
url	https://dx.doi.org/10.1007/s10489-020-01821-1
remote_bool	true
author2	Ge, Huibin Li, Jiayi Wang, Li
author2Str	Ge, Huibin Li, Jiayi Wang, Li
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s10489-020-01821-1
up_date	2024-07-04T02:18:53.380Z
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Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis

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