【論】Wang,2007,Improved centroids estimation for ～

Sijian Wang and Ji Zhu
Improved centroids estimation for the nearest shrunken centroid classifier
Bioinformatics 2007 23(8):972-979
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・サンプルクラス分け法のNSCを改良した、ALP-NSCとAHP-NSCの二方法を提案する。
・データ
0.人工データ
1.Leukemia [Golub]
2.SRBCT [Kahn]
3.NCI-60 [Dudoit]
・比較法
1.NSC (Nearest shrunken centroid)
2.NSC-Ada (NSC with adaptive thresholds)
3.ALP-NSC (Adaptive L-norm penalized NSC)
4.AHP-NSC (Adaptive hierarchically penalized NSC)
・評価法
1.Cross-validation (10-fold, 8-fold)
2.Random split、100回繰り返し

・NSCとは「The NSC uses ‘shrunken’ centroids as prototypes for each class and identifies subsets of genes that best characterize each class.」
・概要「In this article, we re-derive the NSC method as a LASSO regression on gene expression profiles. This re-interpretation allows us to notice that the L1-norm penalty used by NSC may not be the most effective way in analyzing microarray data.（中略）Enlightened by these observations, we consider two different penalty functions different from the L1-norm penalty to make use of natural grouping information within the data.」