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IEEE Transactions on Neural Networks
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Side information, like must-link (ML) and cannot-link (CL), has been widely used in single-view classification tasks. However, so far such information has never been applied in multi-view classification tasks. In many real world situations, data with multiple representations or views are frequently encountered, and most proposed algorithms for such learning situations require that all the multi-view data should be paired. Yet this requirement is difficult to satisfy in some settings and the multi-view data could be totally unpaired. In this paper, we propose an learning framework to design the multi-view classifiers by only employing the weak side information of cross-view must-links (CvML) and cross-view cannot-links (CvCL). The CvML and the CvCL generalize the traditional single-view must-link (SvML) and single-view cannot-link (SvCL), and to the best of our knowledge, are first definitely introduced and applied into the multi-view classification situations. Finally, we demonstrate the effectiveness of our method in our experiments.