Abstract
The subspace methods of classification are decision-theoretic pattern recognition methods in which each class is represented in terms of a linear subspace of the Euclidean pattern or feature space. In most reported subspace methods, a priori criteria have been applied to improve either the class representation or the discriminatory power of the subspaces. Recently, construction of the class subspaces by learning has been suggested by Kohonen, resulting in an improved classification accuracy. A variant of the original learning rule is analyzed and results are given on its application to the classification of phonemes in automatic speech recognition.
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