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Dimensionality Reduction of Clustered Data Sets

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1 Author(s)
Sanguinetti, G. ; Univ. of Sheffield, Sheffield

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.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:30 ,  Issue: 3 )

Date of Publication:

March 2008

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