Nonlinear dimensionality reduction for face recognition
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Palmprint recognition combining LDA and the center band of Fourier magnitude
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Linear discriminant analysis for signatures
IEEE Transactions on Neural Networks
Optimal regularization parameter estimation for regularized discriminant analysis
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Separable linear discriminant analysis
Computational Statistics & Data Analysis
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
Global plus local: A complete framework for feature extraction and recognition
Pattern Recognition
Double linear regressions for single labeled image per person face recognition
Pattern Recognition
A Rayleigh-Ritz style method for large-scale discriminant analysis
Pattern Recognition
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High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is a well-known method for supervised dimensionality reduction. When dealing with high-dimensional and low sample size data, classical LDA suffers from the singularity problem. Over the years, many algorithms have been developed to overcome this problem, and they have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed, which elucidates the properties of various algorithms and their relationships. Based on the proposed framework, we show that the matrix computations involved in LDA-based algorithms can be simplified so that the cross-validation procedure for model selection can be performed efficiently. We conduct extensive experiments using a collection of high-dimensional data sets, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.