Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/47629
Title: INCREMENTAL AND REGULARIZED LINEAR DISCRIMINANT ANALYSIS
Authors: WANG XIAOYAN
Keywords: Dimensionality reduction, linear discriminant analysis, incremental LDA, orthogonal LDA, regularized OLDA, QR factorization, classification accuracy
Issue Date: 23-Aug-2012
Source: WANG XIAOYAN (2012-08-23). INCREMENTAL AND REGULARIZED LINEAR DISCRIMINANT ANALYSIS. ScholarBank@NUS Repository.
Abstract: Linear discriminant analysis (LDA) is a well-known supervised dimensionality reduction technique, which has been applied successfully in many important applications such as pattern recognition, information retrieval, face recognition, micro-array data analysis and text classification. In this thesis, by deriving the mathematical relationship between orthogonal linear discriminant analysis (OLDA) and regularized orthogonal linear discriminant analysis (ROLDA), we find a mathematical criterion for selecting the regularization parameter in ROLDA. Unlike other regularized LDA methods, no candidate set of regularization parameter is needed in our new proposed method. In addition, an LDA-based incremental dimensionality reduction algorithm, ILDA/QR, has been developed, in which bursts of data that contains new classes is being inserted in the form of random chunks (one by one or chunk by chunk).
URI: http://scholarbank.nus.edu.sg/handle/10635/47629
Appears in Collections:Ph.D Theses (Open)

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