Recognizing Handwritten Digits Using Mixtures of Linear Models

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 componentsanalysis(PCA). Incorporating tangent-planeinformation [12] about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance.

• Type: Conference
• Year: 1994
• Where: NIPS
• Authors: Geoffrey E. Hinton, Michael Revow, Peter Dayan
• Keywords: Linear Generative Models, NIPS 1994, NIPS 2007, Pixel-based Images, Sample Covariance Matrices