Machine Learning

, Volume 79, Issue 1–2, pp 47–71

Multi-view kernel construction

  • Virginia R. de Sa
  • Patrick W. Gallagher
  • Joshua M. Lewis
  • Vicente L. Malave
Open Access
Article

DOI: 10.1007/s10994-009-5157-z

Cite this article as:
de Sa, V.R., Gallagher, P.W., Lewis, J.M. et al. Mach Learn (2010) 79: 47. doi:10.1007/s10994-009-5157-z

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.

Spectral clustering Minimizing-disagreement Multi-view fMRI analysis Kernel Canonical correlation analysis CCA Co-clustering 

Copyright information

© The Author(s) 2009

Authors and Affiliations

  • Virginia R. de Sa
    • 1
  • Patrick W. Gallagher
  • Joshua M. Lewis
  • Vicente L. Malave
  1. 1.Department of Cognitive ScienceUniversity of CaliforniaSan DiegoUSA

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