Independent Component Analysis and Bayes' Theorem for robotics and automation

Abstract

Independent Component Analysis (ICA) provides a pragmatic means to perform pattern classification using Bayes' Theorem. Use of ICA with Bayes' Theorem is reviewed and illustrated with examples from classification of images. It is described how ICA with Bayes can create a pattern-classification system that is trainable merely by presenting examples. A specific algorithmic approach is advocated, and demonstrations of its versatility and ease of use show how this technique offers promise for industrial applications.