Two Methods for Estimating Overcomplete Independent Component Bases
Mika Inki, Aapo Hyvärinen · 2001
Estimating overcomplete ICA bases is a difficult problem that emerges when using ICA on many kinds of natural data, e.g. image data. Most algorithms are based on approximations of the likelihood, which leads to computationally heavy procedures. Here we introduce two algorithms that are based on heuristic approximations and estimate an approximate overcomplete basis quite fast. The algorithms are based on quasi-orthogonality in high-dimensional spaces, and the gaussianization procedure, respectively.