Issue No. 03 - March (2008 vol. 30)
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.
dimensionality reduction, clustering, discriminant analysis, probabilistic algorithms
Guido Sanguinetti, "Dimensionality Reduction of Clustered Data Sets", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 535-540, March 2008, doi:10.1109/TPAMI.2007.70819