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Co-regularized kernel k-means for multi-view clustering

In clustering applications, multiple views of the data are often available. Although clustering could be done within each view independently, exploiting information across views is promising to gain clustering accuracy improvement. A common assumption in the field of multi-view learning is that the clustering results from multiple views should be consistent with a latent clustering. However, the potential noise among some views would make this assumption difficult to be satisfied, which finally hurts the clustering performance. To address this issue, we propose a novel clustering algorithm where the intrinsic clustering is found by maximizing the sum of weighted similarities between clusterings of different views. Weights that indicate the qualities of views are learned simultaneously along with the latent clustering and clusterings of different views. A three-step alternative algorithm is designed to solve the problem efficiently. Empirical comparisons with a number of baselines on various datasets confirm the efficacy of our approach.