## Recognizing handwritten digits using mixtures of linear models (1995)

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Venue: | Advances in Neural Information Processing Systems 7 |

Citations: | 56 - 6 self |

### BibTeX

@INPROCEEDINGS{Hinton95recognizinghandwritten,

author = {Geoffrey E Hinton and Michael Revow and Peter Dayan},

title = {Recognizing handwritten digits using mixtures of linear models},

booktitle = {Advances in Neural Information Processing Systems 7},

year = {1995},

pages = {1015--1022},

publisher = {MIT Press}

}

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### 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 [12] about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance. 1

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