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Multi-view clustering has received much attention recently. Most of theexisting multi-view clustering methods only focus on one-sided clustering. Asthe co-occurring data elements involve the counts of sample-featureco-occurrences, it is more efficient to conduct two-sided clustering along thesamples and features simultaneously. To take advantage of two-sided clusteringfor the co-occurrences in the scene of multi-view clustering, a two-sidedmulti-view clustering method is proposed, i.e., multi-viewinformation-theoretic co-clustering (MV-ITCC). The proposed method realizestwo-sided clustering for co-occurring multi-view data under the formulation ofinformation theory. More specifically, it exploits the agreement anddisagreement among views by sharing a common clustering results along thesample dimension and keeping the clustering results of each view specific alongthe feature dimension. In addition, the mechanism of maximum entropy is alsoadopted to control the importance of different views, which can give a rightbalance in leveraging the agreement and disagreement. Extensive experiments areconducted on text and image multi-view datasets. The results clearlydemonstrate the superiority of the proposed method.

Multi-view Information-theoretic Co-clustering for Co-occurrence Data