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Co-regularized Spectral Clustering with Multiple Kernels

Co-regularized Spectral Clustering with Multiple Kernels

This video was recorded at NIPS Workshops, Whistler 2010. We propose a co-regularization based multiview spectral clustering algorithm which enforces the clusterings across multiple views to agree with each-other. Since each view can be used to define a similarity graph over the data, our algorithm can also be considered as learning with multiple similarity graphs, or equivalently with multiple kernels. We propose an objective function that implicitly combines two (or more) kernels, and leads to an improved clustering performance. Experimental comparisons with a number of baselines on several datasets establish the efficacy of our proposed approach.


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