ecognizing Handwritten Digits Using Mixtures of
Linear Models
Geoff Hinton Mike Revow Peter Dayan
In NIPS 7, 1015-1022.
Abstract
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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