# 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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