Stability Analysis of Regularized Linear Discriminant Analysis

• Main Authors: Chih-Han Shih, 施智涵
• Other Authors: 陳素雲
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/72k938

碩士 === 國立臺灣大學 === 應用數學科學研究所 === 106 === Fisher linear discriminant analysis (LDA) is commonly used in classification problems. However, in high dimension low sample size (HDLSS) scenarios, the within-class sample covariance matrix is often singular, which leads to the failure of LDA. Several discriminant methods were developed in literature to deal with this difficulty, such as PCA-LDA, Null-space LDA, Eigen-sparsity based LDA and Ridge LDA. In this thesis, we analyze the stability for various regularized estimators of discriminant direction derived from different methods.