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Abstract: Dimensionality reduction of empirical co-occurrence data is a fundamental problem in unsupervised learning. It is also a well studied problem in statistics known as the analysis of cross-classified data. One principled approach to this problem is to represent the data in low dimension with minimal loss of (mutual) information contained in the original data. In this paper we introduce an information theoretic nonlinear method for finding such a most informative dimension reduction. (Update)
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BibTeX entry: (Update)
@misc{ gamir-journal,
author = "Amir Globerson Gamir",
title = "Journal of Machine Learning Research 3 (2003) 1307-1331 Submitted 5/02;
Published 3/03 Sufficient Dimensionality Reduction",
url = "citeseer.ist.psu.edu/766086.html" }
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