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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hu, Y., Zhu, F., Lv, H., Zhang, X. (2006). Handwritten Digit Recognition Using Low Rank Approximation Based Competitive Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_42
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DOI: https://doi.org/10.1007/11760023_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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