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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제5권 제1호
- 발행연도
- 2003.1
- 수록면
- 17 - 26 (10page)
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Principal component regression (PCR) and partial least squares regression (PLSR) are examined. They usually appear in completely different computational forms in the literature. Since PLSR procedure is always given as an iterative algorithm, it is difficult to see its relationship with PCR. In this paper, alternative unifying expressions of the regression vectors for PCR and PLSR are derived using singular value decomposition of the predictor variables matrix. They are non-iterative and comparative expressions and enable us to gain some insight into the interrelationships between those methods.
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