Multi-view clustering based on a multimetric matrix fusion method

Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering perfo...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yao, Liang [verfasserIn] Lu, Gui-Fu [verfasserIn] Zhao, JinBiao [verfasserIn] Cai, Bing [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Multi-view clustering Joint representation Multimetric matrix Optimal metric matrix

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 228
Übergeordnetes Werk:	volume:228

DOI / URN:	10.1016/j.eswa.2023.120272

Katalog-ID:	ELV010419233

Internformat


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520			\|a Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods.
650		4	\|a Multi-view clustering
650		4	\|a Joint representation
650		4	\|a Multimetric matrix
650		4	\|a Optimal metric matrix
700	1		\|a Lu, Gui-Fu \|e verfasserin \|4 aut
700	1		\|a Zhao, JinBiao \|e verfasserin \|4 aut
700	1		\|a Cai, Bing \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Expert systems with applications \|d Amsterdam [u.a.] : Elsevier Science, 1990 \|g 228 \|h Online-Ressource \|w (DE-627)320577961 \|w (DE-600)2017237-0 \|w (DE-576)11481807X \|7 nnns
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912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
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912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
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912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
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Indexfelder

author_variant	l y ly g f l gfl j z jz b c bc
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hierarchy_sort_str	2023
bklnumber	54.72
publishDate	2023
allfields	10.1016/j.eswa.2023.120272 doi (DE-627)ELV010419233 (ELSEVIER)S0957-4174(23)00774-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yao, Liang verfasserin (orcid)0000-0003-2201-449X aut Multi-view clustering based on a multimetric matrix fusion method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods. Multi-view clustering Joint representation Multimetric matrix Optimal metric matrix Lu, Gui-Fu verfasserin aut Zhao, JinBiao verfasserin aut Cai, Bing verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 228 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 228
spelling	10.1016/j.eswa.2023.120272 doi (DE-627)ELV010419233 (ELSEVIER)S0957-4174(23)00774-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yao, Liang verfasserin (orcid)0000-0003-2201-449X aut Multi-view clustering based on a multimetric matrix fusion method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods. Multi-view clustering Joint representation Multimetric matrix Optimal metric matrix Lu, Gui-Fu verfasserin aut Zhao, JinBiao verfasserin aut Cai, Bing verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 228 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 228
allfields_unstemmed	10.1016/j.eswa.2023.120272 doi (DE-627)ELV010419233 (ELSEVIER)S0957-4174(23)00774-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yao, Liang verfasserin (orcid)0000-0003-2201-449X aut Multi-view clustering based on a multimetric matrix fusion method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods. Multi-view clustering Joint representation Multimetric matrix Optimal metric matrix Lu, Gui-Fu verfasserin aut Zhao, JinBiao verfasserin aut Cai, Bing verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 228 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 228
allfieldsGer	10.1016/j.eswa.2023.120272 doi (DE-627)ELV010419233 (ELSEVIER)S0957-4174(23)00774-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yao, Liang verfasserin (orcid)0000-0003-2201-449X aut Multi-view clustering based on a multimetric matrix fusion method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods. Multi-view clustering Joint representation Multimetric matrix Optimal metric matrix Lu, Gui-Fu verfasserin aut Zhao, JinBiao verfasserin aut Cai, Bing verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 228 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 228
allfieldsSound	10.1016/j.eswa.2023.120272 doi (DE-627)ELV010419233 (ELSEVIER)S0957-4174(23)00774-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yao, Liang verfasserin (orcid)0000-0003-2201-449X aut Multi-view clustering based on a multimetric matrix fusion method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods. Multi-view clustering Joint representation Multimetric matrix Optimal metric matrix Lu, Gui-Fu verfasserin aut Zhao, JinBiao verfasserin aut Cai, Bing verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 228 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:228 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 228
language	English
source	Enthalten in Expert systems with applications 228 volume:228
sourceStr	Enthalten in Expert systems with applications 228 volume:228
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Multi-view clustering Joint representation Multimetric matrix Optimal metric matrix
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Yao, Liang @@aut@@ Lu, Gui-Fu @@aut@@ Zhao, JinBiao @@aut@@ Cai, Bing @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV010419233
language_de	englisch
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author	Yao, Liang
spellingShingle	Yao, Liang ddc 004 bkl 54.72 misc Multi-view clustering misc Joint representation misc Multimetric matrix misc Optimal metric matrix Multi-view clustering based on a multimetric matrix fusion method
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topic_title	004 VZ 54.72 bkl Multi-view clustering based on a multimetric matrix fusion method Multi-view clustering Joint representation Multimetric matrix Optimal metric matrix
topic	ddc 004 bkl 54.72 misc Multi-view clustering misc Joint representation misc Multimetric matrix misc Optimal metric matrix
topic_unstemmed	ddc 004 bkl 54.72 misc Multi-view clustering misc Joint representation misc Multimetric matrix misc Optimal metric matrix
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title	Multi-view clustering based on a multimetric matrix fusion method
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title_full	Multi-view clustering based on a multimetric matrix fusion method
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title_sort	multi-view clustering based on a multimetric matrix fusion method
title_auth	Multi-view clustering based on a multimetric matrix fusion method
abstract	Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods.
abstractGer	Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods.
abstract_unstemmed	Multi-view clustering(MVC) utilizes the consistency of multiple views to learn a consensus representation. However, the existing MVC methods usually use only a single metric to learn the graph matrix, which cannot fully reveal the real structure between complex samples and makes the clustering performance unsatisfactory. To solve this problem, we propose a novel method, i.e., multi-view clustering based on a multimetric matrix fusion method(MVC3MF). Specifically, we first concatenate the multi-view data into a joint representation. Then, we learn multiple kinds of distance metric matrices based on the joint representation. Third, we fuse the obtained multimetric matrices into an optimal metric matrix by using an adaptive weight method. Finally, we use the optimal metric matrix to learn the graph matrix, which is imposed by a rank constraint. To verify the superiority of our algorithm, we performed experiments on six datasets and the experimental results show that the clustering performance of MVC3MF is superior to that of some state-of-the-art MVC methods.
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title_short	Multi-view clustering based on a multimetric matrix fusion method
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author2	Lu, Gui-Fu Zhao, JinBiao Cai, Bing
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doi_str	10.1016/j.eswa.2023.120272
up_date	2024-07-06T17:55:31.026Z
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Multi-view clustering based on a multimetric matrix fusion method

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