【論】Huang,2003,Linear regression and two-class cla～

Xiaohong Huang and Wei Pan
Linear regression and two-class classification with gene expression data
Bioinformatics Vol. 19 no. 16 2003 pages 2072-2078
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・これまで提案されているクラス分け法として、Linear regression model の3方法を取り上げ考察を加え、新たに Partial least squares (PLS)と Penalized PLS (PPLS) の2法を提案する。
・データ
1.Leukemia data [Golub]
2.Colon data [Alon]
・クラス分け比較法(Linear regression model)
1.Weighted voting method [Golub, Tukey]
2.Compound covariate method [Hedenfalk]
3.Shrunken centroids method [Tibshirani]

・問題点「However, most of the existing variable selection schemes are based on univariate analyses, and they proceed in a sequential way because it is computationally too demanding to do best subset selection for large data sets, and hence may not be optimal.」