Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Unsupervised Feature Selection Method Based on Principal Component Loading Vectors

주성분 분석 로딩 벡터 기반 비지도 변수 선택 기법

  • 박영준 (고려대학교 산업경영공학과) ;
  • 김성범 (고려대학교 산업경영공학과)
  • Received : 2013.12.26
  • Accepted : 2014.05.17
  • Published : 2014.06.15
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Abstract

One of the most widely used methods for dimensionality reduction is principal component analysis (PCA). However, the reduced dimensions from PCA do not provide a clear interpretation with respect to the original features because they are linear combinations of a large number of original features. This interpretation problem can be overcome by feature selection approaches that identifying the best subset of given features. In this study, we propose an unsupervised feature selection method based on the geometrical information of PCA loading vectors. Experimental results from a simulation study demonstrated the efficiency and usefulness of the proposed method.

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References

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