| Summary: | 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.
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