Dimensionality Reduction of Clustered Data Sets

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Dimensionality Reduction of Clustered Data Sets
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalization of linear discriminant analysis. This provides a completely new approach to one of the most established and widely used classification algorithms. The performance of the model is then demonstrated on a number of real and artificial data sets.
Guido Sanguinetti
Added 28 Dec 2010
Updated 28 Dec 2010
Type Journal
Year 2008
Where PAMI
Authors Guido Sanguinetti
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