Unsupervised Classification Learning from Cross-Modal Environmental Structure

Abstract

This dissertation addresses the problem of unsupervised learning for pattern classification or category learning. A model that is based on gross cortical anatomy and implements biologically plausible computations is developed and shown to have classification power approaching that of a supervised discriminant algorithm. The advantage of supervised learning is that the final error metric is available during training. Unfortunately, when modeling human category learning, or in constructing classifiers for autonomous robots, one must deal with not having an omniscient entity labeling all incoming sensory patterns. We show that we can substitute for the labels by making use of structure between the pattern distributions to different sensory modalities. For example the co-occurrence of a visual image of a cow with a "moo" sound can be used to simultaneously develop appropriate visual features for distinguishing the cow image and appropriate auditory features for recognizing the moo. We mode...