We study the problem of learning mixtures of distributions, a natural formalization of clustering. A mixture of distributions is a collection of distributions D = {D1, . . . DT },...
Capturing dependencies in images in an unsupervised manner is important for many image processing applications. We propose a new method for capturing nonlinear dependencies in ima...
Abstract--The question of polynomial learnability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoreti...
We consider the problem of learning mixtures of product distributions over discrete domains in the distribution learning framework introduced by Kearns et al. [18]. We give a poly...
Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient FastICA (EFICA) offers an ...