ICA Mixture Models for Unsupervised Classi cation ofNon - Gaussian Sources and Automatic ContextSwitching in Blind Signal Separation

An unsupervised classi cation algorithm is derived from an ICA mixture model assuming that the observed data can be categorized into several mutually exclusive data classes whose components are generated by linear mixtures of independent non-Gaussian sources. The algorithm nds the independent sources, the mixing matrix for each class and also computes the class membership probability for each data point. The new algorithm can improve classi cation accuracy compared with standard Gaussian mixture models. When applied to blind source separation in nonstationary environments, the method can switch automatically between learned mixing matrices. The algorithm can learn e cient codes to represent images containing both natural scenes and text. This method shows promise for modeling structure in high-dimensional data and has many potential applications.