An Efficient Feature Selection Method For Object Detection

概要

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Feature selection is one of the important tasks in many object detection systems because it can improve performance and speed of classifiers. In this paper, we present 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 principle components computed from 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.
社団法人電子情報通信学会の論文
2005-06-10