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
Autor*in: |
Yao, Liang [verfasserIn] Lu, Gui-Fu [verfasserIn] Zhao, JinBiao [verfasserIn] Cai, Bing [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 228 |
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Übergeordnetes Werk: |
volume:228 |
DOI / URN: |
10.1016/j.eswa.2023.120272 |
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Katalog-ID: |
ELV010419233 |
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245 | 1 | 0 | |a Multi-view clustering based on a multimetric matrix fusion method |
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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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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 |
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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 |
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title_full |
Multi-view clustering based on a multimetric matrix fusion method |
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Yao, Liang |
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Expert systems with applications |
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Expert systems with applications |
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eng |
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000 - Computer science, information & general works |
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2023 |
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Yao, Liang Lu, Gui-Fu Zhao, JinBiao Cai, Bing |
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Elektronische Aufsätze |
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Yao, Liang |
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10.1016/j.eswa.2023.120272 |
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(ORCID)0000-0003-2201-449X |
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004 |
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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 |
remote_bool |
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author2 |
Lu, Gui-Fu Zhao, JinBiao Cai, Bing |
author2Str |
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doi_str |
10.1016/j.eswa.2023.120272 |
up_date |
2024-07-06T17:55:31.026Z |
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