Handwritten digit recognition using low rank approximation based competitive neural network

Authors:
Yafeng Hu;Feng Zhu;Hairong Lv;Xianda Zhang
Affiliations:
Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 5
Cited 0

Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
The nature of statistical learning theory

The nature of statistical learning theory
A Handwritten Numeral Character Classification Using Tolerant Rough Set

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Handwritten Digits Using Hierarchical Products of Experts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized low rank approximations of matrices

ICML '04 Proceedings of the twenty-first international conference on Machine learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel approach for handwritten digit recognition is proposed in this paper, which combines the low rank approximation and the competitive neural network together. The images in each class are clustered into several subclasses by the competitive neural network, which is helpful for feature extraction. The low rank approximation is used for image feature extraction. Finally, the k-nearest neighbor classifier is applied to the classification. Experiment results on USPS dataset show the effectiveness of the proposed approach.