The generative Adaptive Subspace Self-Organizing Map

The Adaptive Subspace Self Organized Map (ASSOM) is a model that incorporates sparsity, nonlinear pooling, topological organization and temporal continuity to learn invariant feature detectors, each corresponding to one node of the network. Temporal continuity is implemented by grouping inputs into "training episodes". Each episode contains samples from one invariance class and is mapped to a particular node during training. However, this explicit grouping makes application of this algorithm for natural image sequences difficult, since the grouping is generally not known a priori. This work proposes a probabilistic generative model of the ASSOM that addresses this problem. Each node of the ASSOM generates input vectors from one invariance class. Training sequences are generated by nodes that are chosen according to a Markov process. We demonstrate that this model can learn invariant feature detectors similar to those found in the primary visual cortex from an unlabeled sequence of input images generated by a realistic model of eye movements. Performance is comparable to the original ASSOM algorithm, but without the need for explicit grouping into training episodes.