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Dimensionality Reduction of Clustered Data Sets
March 2008 (vol. 30 no. 3)
pp. 535-540
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 generalisation 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.

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Index Terms:
dimensionality reduction, clustering, discriminant analysis, probabilistic algorithms
Citation:
Guido Sanguinetti, "Dimensionality Reduction of Clustered Data Sets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 535-540, March 2008, doi:10.1109/TPAMI.2007.70819
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