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Recognizing Handwritten Digits Using Mixtures of Linear Models

Geoffrey E Hinton, Michael Revow and Peter Dayan
Department of Computer Science
University of Toronto

We construct a mixture of locally linear generative models of a collection of pixel-based images of digits, and use them for recognition. Different models of a given digit are used to capture different styles of writing, and new images are classified by evaluating their log-likelihoods under each model. We use an EM-based algorithm in which the M-step is computationally straightforward principal components analysis (PCA). Incorporating tangent-plane information about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance.

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Advances in Neural Information Processing Systems 7. G. Tesauro, D. S. Touretzky and T. K. Leen (Eds), pp 1015-1022 MIT Press, Cambridge MA.

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