Multi-view kernel construction

Authors:
Virginia R. Sa;Patrick W. Gallagher;Joshua M. Lewis;Vicente L. Malave
Affiliations:
Department of Cognitive Science, University of California, San Diego, USA 92093-0515;Department of Cognitive Science, University of California, San Diego, USA 92093-0515;Department of Cognitive Science, University of California, San Diego, USA 92093-0515;Department of Cognitive Science, University of California, San Diego, USA 92093-0515
Venue:
Machine Learning
Year:
2010

Citing 0
Cited 4

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Abstract

In many problem domains data may come from multiple sources (or views), such as video and audio from a camera or text on and links to a web page. These multiple views of the data are often not directly comparable to one another, and thus a principled method for their integration is warranted. In this paper we develop a new algorithm to leverage information from multiple views for unsupervised clustering by constructing a custom kernel. We generate a multipartite graph (with the number of parts given by the number of views) that induces a kernel we then use for spectral clustering. Our algorithm can be seen as a generalization of co-clustering and spectral clustering and a relative of Kernel Canonical Correlation Analysis. We demonstrate the algorithm on four data sets: an illustrative artificial data set, synthetic fMRI data, voxels from an fMRI study, and a collection of web pages. Finally, we compare its performance to common alternatives.