Adaptive and structured graph learning for semi-supervised clustering |
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| Institution: | 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 10081, China;2. Scientific Research Base for Integrated Technologies of Precision Agriculture, Ministry of Agriculture, Beijing 10081, China;1. Center for Studies of Information Resources, Wuhan University, Bayi Rd. 299, Wuhan, Hubei 430072, China;2. School of Information Management, Wuhan University, Bayi Rd 299, Wuhan 430072, China |
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| Abstract: | This paper proposes a new method for semi-supervised clustering of data that only contains pairwise relational information. Specifically, our method simultaneously learns two similarity matrices in feature space and label space, in which similarity matrix in feature space learned by adopting adaptive neighbor strategy while another one obtained through tactful label propagation approach. Moreover, the above two learned matrices explore the local structure (i.e., learned from feature space) and global structure (i.e., learned from label space) of data respectively. Furthermore, most of the existing clustering methods do not fully consider the graph structure, they can not achieve the optimal clustering performance. Therefore, our method forcibly divides the data into c clusters by adding a low rank restriction on the graphical Laplacian matrix. Finally, a restriction of alignment between two similarity matrices is imposed and all items are combined into a unified framework, and an iterative optimization strategy is leveraged to solve the proposed model. Experiments in practical data show that our method has achieved brilliant performance compared with some other state-of-the-art methods. |
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| Keywords: | Structured graph learning Semi-supervised clustering Pairwise constraints |
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