title: Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM).
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.
authors.affiliation: Department of Electrical and Computer Engineering, University of Newcastle, NSW 2308, Australia.
authors.forename: B
authors.initials: B
authors.lastname: Zhang
authors.forename: M
authors.initials: M
authors.lastname: Fu
authors.forename: H
authors.initials: H
authors.lastname: Yan
authors.forename: M A
authors.initials: MA
authors.lastname: Jabri