Abstract
In this paper, a method for the dimensionality reduction, based on generalized learning vector quantization (GLVQ), is applied to handwritten digit recognition. GLVQ is a general framework for classifier design based on the minimum classification error criterion, and it is easy to apply it to dimensionality reduction in feature extraction. Experimental results reveal that the training of both a feature transformation matrix and reference vectors by GLVQ is superior to that by principal component analysis in terms of dimensionality reduction.
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Sato, A. (2001). Discriminative Dimensionality Reduction Based on Generalized LVQ. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_10
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DOI: https://doi.org/10.1007/3-540-44668-0_10
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