Multi-view data is common in a wide variety of application domains. Properly exploiting the relations among different views is helpful to alleviate the difficulty of a learning problem of interest. To this end, we propose an extended Probabilistic Latent Semantic Analysis (PLSA) model for multi-view clustering, named Co-regularized PLSA (CoPLSA). CoPLSA integrates individual PLSAs in different views by pairwise co-regularization. The central idea behind the co-regularization is that the sample similarities in the topic space from one view should agree with those from another view. An EM-based scheme is employed for parameter estimation, and a local optimal solution is obtained through an iterative process. Extensive experiments are conducted on three real-world datasets and the compared results demonstrate the superiority of our approach. © 2013 Springer-Verlag.
CITATION STYLE
Jiang, Y., Liu, J., Li, Z., Li, P., & Lu, H. (2013). Co-regularized PLSA for multi-view clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7725 LNCS, pp. 202–213). https://doi.org/10.1007/978-3-642-37444-9_16
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