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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Le, D. D., & Satoh, S. N. ichi. (2005). An efficient feature selection method for object detection. In Lecture Notes in Computer Science (Vol. 3686, pp. 461–468). Springer Verlag. https://doi.org/10.1007/11551188_50
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