Independent Component Analysis and Bayes´ Theorem for robotics and automation

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.