Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM)

Authors:
Bailing Zhang;Minyue Fu;Hong Yan;M. A. Jabri
Affiliations:
Dept. of Electr. & Comput. Eng., Newcastle Univ., NSW;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
1999

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Abstract

The adaptive-subspace self-organizing map (ASSOM) proposed by Kohonen is a recent development in self-organizing map (SOM) computation. In this paper, we propose a method to realize ASSOM using a neural learning algorithm in nonlinear autoencoder networks. Our method has the advantage of numerical stability. We have applied our ASSOM model to build a modular classification system for handwritten digit recognition. Ten ASSOM modules are used to capture different features in the ten classes of digits. When a test digit is presented to all the modules, each module provides a reconstructed pattern and the system outputs a class label by comparing the ten reconstruction errors. Our experiments show promising results. For relatively small size modules, the classification accuracy reaches 99.3% on the training set and over 97% on the testing set