Manifold-based discriminant analysis

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology

Volumn 35, Issue 9, 2013, Pages 2047-2053

  Liu, Zhong Bao ^a   Pan, Guang Zhen ^a   Zhao, Wen Juan ^b  

^a NORTH UNIVERSITY OF CHINA   (China)

^b SHANXI UNIVERSITY   (China)

Author keywords

Dimensionality reduction; Fisher criterion; Global features; Manifold learning; Pattern recognition

Indexed keywords

DIMENSION REDUCTION; DIMENSIONALITY REDUCTION; FISHER CRITERION; GLOBAL FEATURE; HIGH-DIMENSIONAL; LOCAL MANIFOLD STRUCTURE; LOCAL STRUCTURE; MANIFOLD LEARNING;

PATTERN RECOGNITION;

DISCRIMINANT ANALYSIS;

EID: 84885460351     PISSN: 10095896     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1146.2012.01552     Document Type: Article

References (19)

1. Martinez A M and Kak A C. PCA versus LDA[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 228-233.
2. Alibeigi M, Hashemi S, and Hamzeh A. DBFS: an effective density based feature selection scheme for small sample size and high dimensional imbalanced data sets[J]. Data & Knowledge Engineering, 2012, 81/82: 67-103.
3. Friedman H. Regularized discriminant analysis[J]. Journal of the American Statistical Association, 1989, 84(405): 165-175.
4. Li M and Yuan B. 2D-LDA: a novel statistical linear discriminant analysis for image matrix[J]. Pattern Recognition Letters, 2005, 26(5): 527-532.
5. Ye J P and Xiong T. Computational and theoretical analysis of null space and orthogonal linear discriminant analysis[J]. Journal of Machine Learning Research, 2006, 7: 1183-1204.
6. Yu H and Yang J. A direct LDA algorithm for highdimensional data with application to face recognition [J]. Pattern Recognition, 2001, 34(11): 2067-2070.
7. Wan M H, Lai Z H, and Jin Z. Feature extraction using two-dimensional local graph embedding based on maximum margin criterion[J]. Applied Mathematics and Computation, 2011, 217(23): 9659-9668.
8. Ji S W and Ye J P. Generalized linear discriminant analysis: a unified framework and efficient model selection[J]. IEEE Transactions on Neural Networks, 2008, 19(10): 1768-1782.
9. Chen L F, Liao H Y M, Ko M T, et al. A new LDA-based face recognition system which can solve the small sample size problem[J]. Pattern Recognition, 2000, 33(10): 1713-1726.
10. Belhumeur P N, Hespanha J P, and Kriegman D J. Eiegnfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
11. Hastle T, Ruja A, and Tibshirani R. Penalized discriminant analysis[J]. The Annals of Statistics, 1995, 23(1): 73-102.
12. Liu C J and Wechsler H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J]. IEEE Transactions on Image Processing, 2002, 11(4): 467-476.
13. Liu Zhong-bao and Wang Shi-tong. Improved linear discriminant analysis method[J]. Journal of Computer Applications, 2011, 31(1): 250-253.
14. Liu Zhong-bao and Wang Shi-tong. Modified LDA based on linear combination of k-order matrices and its application to face recognition[J]. Computer Engineering and Applications, 2011, 47(12): 152-155.
15. Liu Zhong-bao and Wang Shi-tong. An improved LDA algorithm and its application to face recognition[J]. Computer Engineering and Science, 2011, 33(7): 89-93.
16. He X F and Niyogi P. Locality preserving projections[C]. Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, 2003: 153-160.
17. Roweis S T and Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.
18. Park H. ISOMAP induced manifold embedding and its application to Alzheimer's disease and mild cognitive impairment[J]. Neuroscience Letters, 2012, 513(2): 141-145.
19. Yang J, Zhang D, Yang J Y, et al.. Globally maximizing locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(4): 650-664.