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Multi-View Clustering via Canonical Correlation Analysis
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| Citations: | 75 - 6 self |
BibTeX
@MISC{Chaudhuri_multi-viewclustering,
author = {Kamalika Chaudhuri and Sham M. Kakade},
title = {Multi-View Clustering via Canonical Correlation Analysis},
year = {}
}
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Abstract
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 lower-dimensional subspace, e.g. via Principal Components Analysis (PCA) or random projections, before clustering. Such techniques typically require stringent requirements on the separation between the cluster means (in order for the algorithm to be be successful). Here, we show how using multiple views of the data can relax these stringent requirements. We use Canonical Correlation Analysis (CCA) to project the data in each view to a lower-dimensional subspace. Under the assumption that conditioned on the cluster label the views are uncorrelated, we show that the separation conditions required for the algorithm to be successful are rather mild (significantly weaker than those of prior results in the literature). We provide results for mixture of The multi-view approach to learning is one in which we have ‘views ’ of the data (sometimes in a rather abstract sense) and, if we understand the underlying relationship between these views, the hope is that this relationship can be used to alleviate the difficulty of a learning problem of interest [BM98, KF07, AZ07]. In this work, we explore how having ‘two views ’ of the data makes
