关键词: 广义小样本, 主成分分析（PCA）, 偏最小二乘（PLS）, 特征提取, 特征选择

Abstract: In view of the characteristics of small sample and high dimensional data，Generalized Small Samples（GSS） is defined.It reduces information feature of GSS：feature extraction（dimensionality extraction） and feature selection（dimensionality selection）.Firstly，unsupervised feature extraction based on Principal Component Analysis（PCA） and supervised feature extraction based on Partial Least Squares（PLS） are introduced.Secondly，analyzing the structure of first PC，it presents new global PCA-based and PLS-based feature selection approaches，in addition recursive feature elimination on PLS（PLS-RFE） is realized.Finally，the approaches are applied to the classification of MIT AML/ALL，it performs feature extraction on PCA and PLS，and feature selection compared with PLS-RFE.The information compression of GSS is realized.

Key words: generalized small sample, Principal Component Analysis（PCA）, Partial Least Squares（PLS）, feature extraction, feature selection