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• W2137010418 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." @default.
• W2137010418 created "2016-06-24" @default.
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• W2137010418 date "2003-06-04" @default.
• W2137010418 modified "2023-10-18" @default.
• W2137010418 title "Non-linear feature extraction by the coordination of mixture models" @default.
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• W2137010418 hasPublicationYear "2003" @default.
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