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REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSIS

Abstract

The essence of principal components analysis and the problem of dimension reduction are described. A method of principal components calculation is presented, which is based on the covariance matrix eigenvalues determination. Practical implementations of principal components analysis are described, which are based on QR-algorithm. Application of principal components analysis in space images classification for the reduction of training samples dimension is discussed.

For citations:


Pradun D.V. REDUCTION OF TRAINING SAMPLES DIMENSION IN PATTERN RECOGNITION OF SPACE IMAGES USING PRINCIPAL COMPONENTS ANALYSIS. Informatics. 2013;(1):57-65. (In Russ.)

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ISSN 1816-0301 (Print)
ISSN 2617-6963 (Online)