An efficient kernel discriminant analysis method

Pattern Recognition

Volumn 38, Issue 10, 2005, Pages 1788-1790

  Lu, Juwei ^a   Plataniotis, K N ^a   Venetsanopoulos, A N ^a   Wang, Jie ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

Face recognition; Kernel machine; Regularization; Small sample size

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; EIGENVALUES AND EIGENFUNCTIONS; LEARNING SYSTEMS; MATRIX ALGEBRA; PARAMETER ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; SPURIOUS SIGNAL NOISE;

KERNEL MACHINE; REGULARIZATION; SMALL SAMPLE SIZE (SSS); SUBSPACE DECOMPOSITION;

PATTERN RECOGNITION;

EID: 22844432564     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.02.005     Document Type: Article

References (5)

1. L.-F. Chen, H.-Y.M. Liao, M.-T. Ko, J.-C. Lin, and G.-J. Yu A new LDA-based face recognition system which can solve the small sample size problem Pattern Recogn. 33 2000 1713 1726
2. H. Yu, and J. Yang A direct LDA algorithm for high-dimensional data - with application to face recognition Pattern Recogn. 34 2001 2067 2070
3. J. Lu, K. Plataniotis, and A. Venetsanopoulos Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition Pattern Recogn. Lett. 26 2 2005 181 191
4. G. Baudat, and F. Anouar Generalized discriminant analysis using a kernel approach Neural Computation 12 2000 2385 2404
5. J. Lu, K. Plataniotis, and A. Venetsanopoulos Face recognition using kernel direct discriminant analysis algorithms IEEE Trans. Neural Networks 14 1 2003 117 126