Paper

Recognizing Handwritten Digits Using Mixtures of Linear Models

We construct a mixture of locally linear generative models of a col-lection of pixel-based images of digits, and use them for recogni-tion. 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 informa-tion [12] about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance. 1

Authors: Geoffrey E. Hinton · Michael Revow · Peter Dayan

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