Joint representation learning for multi-view subspace clustering

Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhang, Guang-Yu [verfasserIn] Zhou, Yu-Ren [verfasserIn] Wang, Chang-Dong [verfasserIn] Huang, Dong [verfasserIn] He, Xiao-Yu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Multi-view subspace clustering View-specific representation learning Low-rank tensor representation learning Unified framework

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 166
Übergeordnetes Werk:	volume:166

DOI / URN:	10.1016/j.eswa.2020.113913

Katalog-ID:	ELV005159008

Internformat


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520			\|a Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts.
650		4	\|a Multi-view subspace clustering
650		4	\|a View-specific representation learning
650		4	\|a Low-rank tensor representation learning
650		4	\|a Unified framework
700	1		\|a Zhou, Yu-Ren \|e verfasserin \|4 aut
700	1		\|a Wang, Chang-Dong \|e verfasserin \|4 aut
700	1		\|a Huang, Dong \|e verfasserin \|4 aut
700	1		\|a He, Xiao-Yu \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Expert systems with applications \|d Amsterdam [u.a.] : Elsevier Science, 1990 \|g 166 \|h Online-Ressource \|w (DE-627)320577961 \|w (DE-600)2017237-0 \|w (DE-576)11481807X \|7 nnns
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Indexfelder

author_variant	g y z gyz y r z yrz c d w cdw d h dh x y h xyh
matchkey_str	zhangguangyuzhouyurenwangchangdonghuangd:2020----:onrpeettolannfrutvesb
hierarchy_sort_str	2020
bklnumber	54.72
publishDate	2020
allfields	10.1016/j.eswa.2020.113913 doi (DE-627)ELV005159008 (ELSEVIER)S0957-4174(20)30707-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, Guang-Yu verfasserin aut Joint representation learning for multi-view subspace clustering 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts. Multi-view subspace clustering View-specific representation learning Low-rank tensor representation learning Unified framework Zhou, Yu-Ren verfasserin aut Wang, Chang-Dong verfasserin aut Huang, Dong verfasserin aut He, Xiao-Yu verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 166 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:166 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 166
spelling	10.1016/j.eswa.2020.113913 doi (DE-627)ELV005159008 (ELSEVIER)S0957-4174(20)30707-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, Guang-Yu verfasserin aut Joint representation learning for multi-view subspace clustering 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts. Multi-view subspace clustering View-specific representation learning Low-rank tensor representation learning Unified framework Zhou, Yu-Ren verfasserin aut Wang, Chang-Dong verfasserin aut Huang, Dong verfasserin aut He, Xiao-Yu verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 166 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:166 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 166
allfields_unstemmed	10.1016/j.eswa.2020.113913 doi (DE-627)ELV005159008 (ELSEVIER)S0957-4174(20)30707-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, Guang-Yu verfasserin aut Joint representation learning for multi-view subspace clustering 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts. Multi-view subspace clustering View-specific representation learning Low-rank tensor representation learning Unified framework Zhou, Yu-Ren verfasserin aut Wang, Chang-Dong verfasserin aut Huang, Dong verfasserin aut He, Xiao-Yu verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 166 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:166 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 166
allfieldsGer	10.1016/j.eswa.2020.113913 doi (DE-627)ELV005159008 (ELSEVIER)S0957-4174(20)30707-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, Guang-Yu verfasserin aut Joint representation learning for multi-view subspace clustering 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts. Multi-view subspace clustering View-specific representation learning Low-rank tensor representation learning Unified framework Zhou, Yu-Ren verfasserin aut Wang, Chang-Dong verfasserin aut Huang, Dong verfasserin aut He, Xiao-Yu verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 166 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:166 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 166
allfieldsSound	10.1016/j.eswa.2020.113913 doi (DE-627)ELV005159008 (ELSEVIER)S0957-4174(20)30707-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, Guang-Yu verfasserin aut Joint representation learning for multi-view subspace clustering 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts. Multi-view subspace clustering View-specific representation learning Low-rank tensor representation learning Unified framework Zhou, Yu-Ren verfasserin aut Wang, Chang-Dong verfasserin aut Huang, Dong verfasserin aut He, Xiao-Yu verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 166 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:166 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 166
language	English
source	Enthalten in Expert systems with applications 166 volume:166
sourceStr	Enthalten in Expert systems with applications 166 volume:166
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Multi-view subspace clustering View-specific representation learning Low-rank tensor representation learning Unified framework
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Zhang, Guang-Yu @@aut@@ Zhou, Yu-Ren @@aut@@ Wang, Chang-Dong @@aut@@ Huang, Dong @@aut@@ He, Xiao-Yu @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV005159008
language_de	englisch
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author	Zhang, Guang-Yu
spellingShingle	Zhang, Guang-Yu ddc 004 bkl 54.72 misc Multi-view subspace clustering misc View-specific representation learning misc Low-rank tensor representation learning misc Unified framework Joint representation learning for multi-view subspace clustering
authorStr	Zhang, Guang-Yu
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topic_title	004 DE-600 54.72 bkl Joint representation learning for multi-view subspace clustering Multi-view subspace clustering View-specific representation learning Low-rank tensor representation learning Unified framework
topic	ddc 004 bkl 54.72 misc Multi-view subspace clustering misc View-specific representation learning misc Low-rank tensor representation learning misc Unified framework
topic_unstemmed	ddc 004 bkl 54.72 misc Multi-view subspace clustering misc View-specific representation learning misc Low-rank tensor representation learning misc Unified framework
topic_browse	ddc 004 bkl 54.72 misc Multi-view subspace clustering misc View-specific representation learning misc Low-rank tensor representation learning misc Unified framework
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title	Joint representation learning for multi-view subspace clustering
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title_full	Joint representation learning for multi-view subspace clustering
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title_sort	joint representation learning for multi-view subspace clustering
title_auth	Joint representation learning for multi-view subspace clustering
abstract	Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts.
abstractGer	Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts.
abstract_unstemmed	Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts.
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title_short	Joint representation learning for multi-view subspace clustering
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author2	Zhou, Yu-Ren Wang, Chang-Dong Huang, Dong He, Xiao-Yu
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doi_str	10.1016/j.eswa.2020.113913
up_date	2024-07-06T17:00:50.933Z
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Joint representation learning for multi-view subspace clustering

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