Proceedings of the Korean Statistical Society Conference (한국통계학회:학술대회논문집)
- 2003.05a
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- Pages.151-156
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- 2003
A Penalized Principal Components using Probabilistic PCA
- Park, Chong-Sun (Department of Statistics, Sungkyunkwan University) ;
- Wang, Morgan (Department of Statistics & Actuarial Science, University of Central Florida)
- Published : 2003.05.23
Abstract
Variable selection algorithm for principal component analysis using penalized likelihood method is proposed. We will adopt a probabilistic principal component idea to utilize likelihood function for the problem and use HARD penalty function to force coefficients of any irrelevant variables for each component to zero. Consistency and sparsity of coefficient estimates will be provided with results of small simulated and illustrative real examples.
Keywords
- Principal Component Analysis;
- Variable Selection;
- Penalized Likelihood;
- Hard Thresholding Penalty Function