Multiview Semi-Supervised Learning Model for Image Classification
Semi-supervised learning models for multiview data are important in image classification tasks, since heterogeneous features are easy to obtain and semi-supervised schemes are economical and effective. To model the view importance, conventional graph-based multiview learning models learn a linear co...
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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2020-12, Vol.32 (12), p.2389-2400 |
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| container_title | IEEE transactions on knowledge and data engineering |
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| creator | Nie, Feiping Tian, Lai Wang, Rong Li, Xuelong |
| description | Semi-supervised learning models for multiview data are important in image classification tasks, since heterogeneous features are easy to obtain and semi-supervised schemes are economical and effective. To model the view importance, conventional graph-based multiview learning models learn a linear combination of views while assuming a priori weights distribution. In this paper, we present a novel structural regularized semi-supervised model for multiview data, termed Adaptive MUltiview SEmi-supervised model (AMUSE). Our new model learns weights from a priori graph structure, which is more reasonable than weight regularization. Theoretical analysis reveals the significant difference between AMUSE and the prior arts. An efficient optimization algorithm is provided to solve the new model. Experimental results on six real-world data sets demonstrate the effectiveness of the structural regularized weights learning scheme. |
| doi_str_mv | 10.1109/TKDE.2019.2920985 |
| format | Article |
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To model the view importance, conventional graph-based multiview learning models learn a linear combination of views while assuming a priori weights distribution. In this paper, we present a novel structural regularized semi-supervised model for multiview data, termed Adaptive MUltiview SEmi-supervised model (AMUSE). Our new model learns weights from a priori graph structure, which is more reasonable than weight regularization. Theoretical analysis reveals the significant difference between AMUSE and the prior arts. An efficient optimization algorithm is provided to solve the new model. 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Experimental results on six real-world data sets demonstrate the effectiveness of the structural regularized weights learning scheme.</description><subject>Adaptation models</subject><subject>Algorithms</subject><subject>Computational modeling</subject><subject>Data models</subject><subject>Economic models</subject><subject>Feature extraction</subject><subject>graph-based learning</subject><subject>Image classification</subject><subject>Laplace equations</subject><subject>Multiview learning</subject><subject>Optimization</subject><subject>Regularization</subject><subject>Semi-supervised learning</subject><subject>Semisupervised learning</subject><subject>structured graph</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kFFPwjAUhRujiYj-AOPLEp-HvV27to8EQYkQH8Dnpqx3pGRs2G4Y_70jEJ_uefjOuclHyCPQEQDVL-uP1-mIUdAjphnVSlyRAQihUgYarvtMOaQ84_KW3MW4o5QqqWBAxsuuav3R40-ywr1PV90Bw9FHdMkCbah9vU2WjcMqKZuQzPd2i8mksjH60he29U19T25KW0V8uNwh-ZpN15P3dPH5Np-MF2nBdNamVvMCkCnFHSiKFIVjpeIKtHDCasg3ym4KCeBoZmUhRSY2jJc2d6W0TuTZkDyfdw-h-e4wtmbXdKHuXxrGcwoClFY9BWeqCE2MAUtzCH5vw68Bak6mzMmUOZkyF1N95-nc8Yj4zyuZgeQ0-wPx2GPj</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Nie, Feiping</creator><creator>Tian, Lai</creator><creator>Wang, Rong</creator><creator>Li, Xuelong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptation models Algorithms Computational modeling Data models Economic models Feature extraction graph-based learning Image classification Laplace equations Multiview learning Optimization Regularization Semi-supervised learning Semisupervised learning structured graph |
| title | Multiview Semi-Supervised Learning Model for Image Classification |
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