Ica Mixture Models for Unsupervised Classification and Automatic Context Switching

We present an unsupervised classification algorithm based on an ICA mixture model. A mixture model is a model in which the observed data can be categorized into several mutually exclusive data classes. In an ICA mixture model, it is assumed that the data in each class are generated by a linear mixture of independent sources. The algorithm finds the independent sources and the mixing matrix for each class and also computes the class membership probability of for each data point. This approach extends the Gaussian mixture model so that the clusters can have non-Gaussian structure. Performance on a standard classification problem, the Iris flower data set, demonstrates that the new algorithm can improve classification accurately over standard Gaussian mixture models. We also show that the algorithm can be applied to blind source separation in nonstationary environments. The method can switch automatically between learned miving matrices in different environments.