Deterministic Generative Models for Fast Feature Discovery
We propose a vector quantisation method which does not only provide a compact description of data vectors in terms codebook vectors, but also gives an explanation of codebook vectors as binary combinations of elementary features. This corresponds to the intuitive notion that, in the real world, patterns can be usefully thought of as being constructed by compositions from simpler features. The model can be understood as a generative model, in which the codebook vector is generated by a hidden binary state vector. The model is non-probabilistic in the sense that it assigns each data vector to a single codebook vector. We describe exact and approximate learning algorithms for learning deterministic feature representations. In contrast to probabilistic models, the deterministic approach allows the use of message propagation algorithms within the learning scheme. These are compared with standard mean-field/Gibbs sampling learning. We show that Generative Vector Quantisation gives a good performance in large scale real world tasks like image compression and handwritten digit analysis with up to 400 data dimensions.