Abstract
A neural algorithm for Local Principal Components Analysis (LPCA), i.e. Principal Component Analysis (PCA) performed in data clusters, is presented. It is applied to find local linear models for compact representation of experimental data. The local models are used for pattern recognition what gives a new recognition procedure called local subspace method. For handwritten numerals from NIST database, the technique reaches the recognition rate of about 99.4%.
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© 1997 Springer-Verlag Berlin Heidelberg
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Skarbek, W., Ignasiak, K. (1997). Handwritten digit recognition by local principal components analysis. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1997. Lecture Notes in Computer Science, vol 1325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63614-5_21
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DOI: https://doi.org/10.1007/3-540-63614-5_21
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