Prakash, M and Murty, Narasimha M (1997) Growing Subspace Pattern Recognition Methods and Their Neural-Network Models. In: IEEE Transactions on Neural Networks, 8 (1). 161 -168.
In statistical pattern recognition, the decision of which features to use is usually left to human judgment. If possible, automatic methods are desirable. Like multilayer perceptrons, learning subspace methods (LSMs) have the potential to integrate feature extraction and classification. In this paper, we propose two new algorithms, along with their neural-network implementations, to overcome certain limitations of the earlier LSMs. By introducing one cluster at a time and adapting it if necessary, we eliminate one limitation of deciding how many clusters to have in each class by trial-and-error. By using the principal component analysis neural networks along with this strategy, we propose neural-network models which are better in overcoming another limitation, scalability. Our results indicate that the proposed classifiers are comparable to classifiers like the multilayer perceptrons and the nearest-neighbor classifier in terms of classification accuracy. In terms of classification speed and scalability in design, they appear to be better for large-dimensional problems.
|Item Type:||Journal Article|
|Additional Information:||Copyright 1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Keywords:||Subspace methods;Learning methods;Neural networks; constructive architectures;Character recognition|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||25 Aug 2008|
|Last Modified:||19 Sep 2010 04:25|
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