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ICML
2009
IEEE

Multi-view clustering via canonical correlation analysis

13 years 14 days ago
Multi-view clustering via canonical correlation analysis
Clustering data in high dimensions is believed to be a hard problem in general. A number of efficient clustering algorithms developed in recent years address this problem by projecting the data into a lowerdimensional subspace, e.g. via Principal Components Analysis (PCA) or random projections, before clustering. Here, we consider constructing such projections using multiple views of the data, via Canonical Correlation Analysis (CCA). Under the assumption that the views are uncorrelated given the cluster label, we show that the separation conditions required for the algorithm to be successful are significantly weaker than prior results in the literature. We provide results for mixtures of Gaussians and mixtures of log concave distributions. We also provide empirical support from audio-visual speaker clustering (where we desire the clusters to correspond to speaker ID) and from hierarchical Wikipedia document clustering (where one view is the words in the document and the other is the ...
Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu,
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan
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