Abstract
We propose a simple yet efficient feature-selection method — based on principle component analysis (PCA) — for SVM-based classifiers. The idea is to select features whose corresponding axes are closest to the principle components computed from a data distribution by PCA. Experimental results show that our proposed method reduces dimensionality similar to PCA, but maintains the original measurement meanings while decreasing the computation time significantly.
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Le, DD., Satoh, S. (2005). An Efficient Feature Selection Method for Object Detection. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_50
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DOI: https://doi.org/10.1007/11551188_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
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