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An Efficient Feature Selection Method for Object Detection

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Pattern Recognition and Data Mining (ICAPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3686))

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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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© 2005 Springer-Verlag Berlin Heidelberg

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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