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The Application of SVD for Feature Extraction  

Lee Hyun-Seung (Samsung Electronics)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.43, no.2, 2006 , pp. 82-86 More about this Journal
Abstract
The design of a pattern recognition system generally involves the three aspects: preprocessing, feature extraction, and decision making. Among them, a feature extraction method determines an appropriate subspace of dimensionality in the original feature space of dimensionality so that it can reduce the complexity of the system and help to improve successful recognition rates. Linear transforms, such as principal component analysis, factor analysis, and linear discriminant analysis have been widely used in pattern recognition for feature extraction. This paper shows that singular value decomposition (SVD) can be applied usefully in feature extraction stage of pattern recognition. As an application, a remote sensing problem is applied to verify the usefulness of SVD. The experimental result indicates that the feature extraction using SVD can improve the recognition rate about 25% compared with that of PCA.
Keywords
Pattern Recognition; Feature Extraction; SVD;
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