- M. Shashanka, B. Raj, P. Smaragdis, P. I. 2009. Probabilistic Latent Variable Model for Sparse Decompositions of Non-negative Data. Abstract: An important problem in data-analysis tasks is to find suitable representations that make hidden structure inthe data explicit. In this paper, we present a probabilistic latent variable model that is equivalent to a matrix decomposition of no nnegative data. Data is modeled as histograms of multiple dra ws from an underlying generative process. The model expresses the generative distribution as a mixture of hidden distributions which capture the latent structure. We extend the model to incorporate sparsity constraints by using an entropic prior. We derive algorithms for parameter estimation and show how the model can be applied for unsupervised feature extraction an d supervised classification tasks.
