LMS and LTS-type Alternatives to Classical Principal Component Analysis
LMS and LTS-type Alternatives to Classical Principal Component Analysis
- 주제(키워드) Principal component analysis (PCA) , Projection pursuit , Least squares (LS) , Least median of squares (LMS) , Least trimmed squares (LTS).
- 발행기관 한국통계학회
- 발행년도 2006
- 총서유형 Journal
- UCI G704-000420.2006.13.2.014
- KCI ID ART001021565
- 본문언어 영어
초록/요약
Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.
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