Non-linear feature extraction by the coordination of mixture models

Jakob Verbeek; Nikos Vlassis; Ben Krose

Non-linear feature extraction by the coordination of mixture models

Jakob Verbeek ¹ Nikos Vlassis ¹ Ben Krose ¹

Abstract : We present a method for non-linear data projection that offers non-linear versions of Principal Component Analysis and Canonical Correlation Analysis. The data is accessed through a probabilistic mixture model only, therefore any mixture model for any type of data can be plugged in. Gaussian mixtures are one example, but mixtures of Bernoulli's to model discrete data might be used as well. The algorithm minimizes an objective function that exhibits one global optimum that can be found by finding the eigenvectors of some matrix. Experimental results on toy data and real data are provided.

Keywords : Canonical Correlation Analysis Principal Component Analysis Self-organizing Maps

Document type :

Conference papers

Domain :

Computer Science [cs] / Machine Learning [cs.LG]

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https://hal.inria.fr/inria-00321490
Contributor : Jakob Verbeek <>
Submitted on : Tuesday, March 8, 2011 - 3:04:10 PM
Last modification on : Monday, September 25, 2017 - 10:08:04 AM
Long-term archiving on : Thursday, June 9, 2011 - 2:45:01 AM

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Non-linear feature extraction by the coordination of mixture models

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