Multi-view clustering via canonical correlation analysis

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Multi-view clustering via canonical correlation analysis is a 2009 conference paper written in English by Chaudhuri K., Kakade S.M., Livescu K., Sridharan K. and published in ACM International Conference Proceeding Series.

[edit] Abstract

Clustering data in high dimensions is believed to be a hard problem in general. A number of effcient 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 signicantly 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 link structure). Copyright 2009.

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