Dimensional reduction of clustered data sets

We present a novel probabilistic latent variable model to perform linear dimensional reduction on data sets which contain clusters. The model simultaneously performs the clustering and the dimensional reduction in an unsupervised manner, providing an optimal visualisation of the clusterig. We prove that the resulting dimensional reduction results in Fisher’s linear discriminant analysis as a limiting case. Testing of the model both on real and artificial data shows marked improvements in the quality of the visualisation and clustering.