Fast, Large-Scale Transformation-Invariant Clustering

Abstract

In previous work on transformed mixtures of Gaussians'' andtransformed hidden Markov models'', we showed how the EM al- gorithm in a discrete latent variable model can be used to jointly normalize data (e.g., center images, pitch-normalize spectrograms) and learn a mixture model of the normalized data. The only input to the algorithm is the data, a list of possible transformations, and the number of clusters to find. The main criticism of this work was that the exhaustive computation of the posterior probabili- ties over transformations would make scaling up to large feature vectors and large sets of transformations intractable. Here, we de- scribe how a tremendous speed-up is acheived through the use of a variational technique for decoupling transformations, and a fast Fourier transform method for computing posterior probabilities. For NN images, learning C clusters under N rotations, N scales,