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21

PAMI
2008

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Dimensionality Reduction of Clustered Data Sets

13 years 4 months ago

Dimensionality Reduction of Clustered Data Sets

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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

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Data Sets | Linear Dimensionality Reduction | Maximum Likelihood Solution | PAMI 2006 | PAMI 2008 |

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Added	28 Dec 2010
Updated	28 Dec 2010
Type	Journal
Year	2008
Where	PAMI
Authors	Guido Sanguinetti

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