Factorial Learning by Clustering Features

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Joshua Tenenbaum, Emanuel V. Todorov


We introduce a novel algorithm for factorial learning, motivated by segmentation problems in computational vision, in which the underlying factors correspond to clusters of highly correlated input features. The algorithm derives from a new kind of competitive clustering model, in which the cluster generators compete to ex(cid:173) plain each feature of the data set and cooperate to explain each input example, rather than competing for examples and cooper(cid:173) ating on features, as in traditional clustering algorithms. A natu(cid:173) ral extension of the algorithm recovers hierarchical models of data generated from multiple unknown categories, each with a differ(cid:173) ent, multiple causal structure. Several simulations demonstrate the power of this approach.