Incomplete Multi-View Clustering With Sample-Level Auto-Weighted Graph Fusion
Incomplete multi-view clustering (IMC) has received considerable attention due to its flexibility in fusing the multi-view information when the view samples are partly missing. However, existing methods seldom consider the affection of the missing samples to the contributions of the views. In this p...
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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2023-06, Vol.35 (6), p.6504-6511 |
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| container_title | IEEE transactions on knowledge and data engineering |
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| creator | Liang, Naiyao Yang, Zuyuan Xie, Shengli |
| description | Incomplete multi-view clustering (IMC) has received considerable attention due to its flexibility in fusing the multi-view information when the view samples are partly missing. However, existing methods seldom consider the affection of the missing samples to the contributions of the views. In this paper, we propose a novel graph fusion based IMC model (SAGF_IMC) to handle this problem. Instead of directly weighting the whole view, SAGF_IMC learns the sample-level auto weight, which allows considering both the contributions of different views and the affection of the missing samples. An effective iterative algorithm is developed, together with its convergence analysis. Experiments are provided to demonstrate that SAGF_IMC is superior to the related state-of-the-art methods by using several real-world datasets. |
| doi_str_mv | 10.1109/TKDE.2022.3171911 |
| format | Article |
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However, existing methods seldom consider the affection of the missing samples to the contributions of the views. In this paper, we propose a novel graph fusion based IMC model (SAGF_IMC) to handle this problem. Instead of directly weighting the whole view, SAGF_IMC learns the sample-level auto weight, which allows considering both the contributions of different views and the affection of the missing samples. An effective iterative algorithm is developed, together with its convergence analysis. 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Experiments are provided to demonstrate that SAGF_IMC is superior to the related state-of-the-art methods by using several real-world datasets.</description><subject>Analytical models</subject><subject>auto-weight learning</subject><subject>Clustering</subject><subject>Computational modeling</subject><subject>Convergence</subject><subject>Fuses</subject><subject>graph fusion</subject><subject>Incomplete multi-view clustering</subject><subject>Indexes</subject><subject>Iterative algorithms</subject><subject>Iterative methods</subject><subject>Laplace equations</subject><subject>Optimization</subject><subject>sample weight</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1PwkAQhhujiYj-AOOliefF3e5njwQBiRAPohw323YKS0qLu1uN_942EE_zTvK8M8kTRfcEjwjB6dP69Xk6SnCSjCiRJCXkIhoQzhVKuuWyy5gRxCiT19GN93uMsZKKDKLVos6bw7GCAPGqrYJFnxZ-4knV-gDO1tt4Y8Mufjc9g5bwDVU8bkODNmC3uwBFPHfmuItnrbdNfRtdlabycHeew-hjNl1PXtDybb6YjJcoTxIakChSykrKOKEKFyKFLMUiE5QZkhWykJLnJTdlRsHwDlQEhKGpKRmTXBDgdBg9nu4eXfPVgg9637Su7l7qRGGlKGap6ihyonLXeO-g1EdnD8b9aoJ1b0331nRvTZ-tdZ2HU8cCwD-fSiGFEvQPF1ZnvA</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Liang, Naiyao</creator><creator>Yang, Zuyuan</creator><creator>Xie, Shengli</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Analytical models auto-weight learning Clustering Computational modeling Convergence Fuses graph fusion Incomplete multi-view clustering Indexes Iterative algorithms Iterative methods Laplace equations Optimization sample weight |
| title | Incomplete Multi-View Clustering With Sample-Level Auto-Weighted Graph Fusion |
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