A direct LDA algorithm for high-dimensional data - with application to face recognition

By Hua Yu, Jie Yang. Pattern Recognition, 2001

1,696 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/04250e037dce3a438d8f49a4400566457190f4e2

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In Analytical Chemistry

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Matching Forensic Sketches to Mug Shot Photos (2011, methodology): “Other discriminant analysis methods have been proposed to handle the SSS problem, such as random sampling LDA [23], regularized LDA [24], and direct LDA [25].”
Learning Distance Metrics with Contextual Constraints for Image Retrieval (2006, methodology): “To obtain the solution effectively, we propose an algorithm to find the optimal transformation matrix, which was used to solve LDA in the previous study [22].”
Efficient and robust feature extraction by maximum margin criterion (2003, background): “The authors would like to thank the associate editor and the anonymous reviewers for the constructive comments.”
Two-dimensional Fisher discriminant analysis: forget about small sample size problem [face recognition applications] (2005, neutral): “2D FDA 2D PCA N LDA [7] FLDA [3] D LDA [6]”
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction (2007, methodology): “Among them, the linear algorithms Principal Component Analysis (PCA) [11], [14], [22] and Linear Discriminant Analysis (LDA) [8], [14], [32], [33] have been the two most popular because of their relative simplicity and effectiveness .”
Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" (2007, methodology): “PCA and LDA are two well-known linear subspace learning methods which have been extensively used in pattern recognition and computer vision areas and have become the most popular techniques for face recognition and other biometrics [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]”
Gabor wavelets and General Discriminant Analysis for face identification and verification (2007, methodology): “Several techniques, such as the PCA + LDA [26], Regularized LDA (RLDA) [27], Enhanced LDA (ELDA) [28] and Direct LDA (DLDA) [29] have been proposed in literature to solve the Small Sample Size (SSS) problem.”
A review on Gabor wavelets for face recognition (2006, methodology): “larized LDA (RLDA) [9], Enhanced LDA (ELDA) [10] and Direct LDA (DLDA) [11].”
High-speed face recognition based on discrete cosine transform and RBF neural networks (2005, methodology): “Compared with the Eigenface (PCA) approach, the fisherface approach is more insensitive to large variations in lighting direction and facial expression.”
2D and 3D face recognition: A survey (2007, methodology): “Thus, in some cases the LDA is applied directly on the input space, as in (Chen et al., 2000; Yu and Yang, 2001).”
A General Exponential Framework for Dimensionality Reduction (2014, methodology): “To tackle the SSS problem , many variants of LDA have been proposed in the recent years, such as Fisherface [3], null LDA [6], LDA/QR [7], LDA/GSVD [8], LDA/FKT [9], DLA [10], [11], Direct LDA, and its variants [6], [12]–[14].”
Geometric Mean for Subspace Selection (2009, methodology): “Index Terms—Arithmetic mean, Fisher’s linear discriminant analysis (FLDA), geometric mean, Kullback-Leibler (KL) divergence, machine learning, subspace selection (or dimensionality reduction), visualization.”
Linear discriminant analysis for the small sample size problem: an overview (2014, positive): “In the DLDA method, the dimensionality is reduced in two stages.”
Dual-space linear discriminant analysis for face recognition (2004, background): “Yu. et al. [3] proposed a direct LDA algorithm.”
A unified framework for subspace face recognition (2004, methodology): “To improve the generalization ability of LDA on different data sets, several modifications are proposed, including the Enhanced FLD model [5], LDA mixture model [3], and direct LDA [ 17 ].”
Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition (2005, methodology): “To prevent this from happening, solutions without a separate PCA step, called direct LDA (D-LDA) methods have been presented recently in (Chen et al., 2000; Yu and Yang, 2001; Lu et al., 2003b).”
KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition (2005, background): “Unfortunately, all of these methods discard the discriminant information contained in the null space of the within-class covariance matrix, yet this discriminant information is very effective for " small sample size " (SSS) problem [21], [22], [23], [24], [25].”