Recognizing Handwritten Digits Using Mixtures of Linear Models
Abstract
We construct a mixture of locally linear generative models of a col(cid:173) lection of pixel-based images of digits, and use them for recogni(cid:173) 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(cid:173) tion [12] about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance.
Cite
Text
Hinton et al. "Recognizing Handwritten Digits Using Mixtures of Linear Models." Neural Information Processing Systems, 1994.Markdown
[Hinton et al. "Recognizing Handwritten Digits Using Mixtures of Linear Models." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/hinton1994neurips-recognizing/)BibTeX
@inproceedings{hinton1994neurips-recognizing,
title = {{Recognizing Handwritten Digits Using Mixtures of Linear Models}},
author = {Hinton, Geoffrey E. and Revow, Michael and Dayan, Peter},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {1015-1022},
url = {https://mlanthology.org/neurips/1994/hinton1994neurips-recognizing/}
}