A new linear discriminant analysis method to address the over-reducing problem

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9124, Issue , 2015, Pages 65-72

  Wan, Huan ^a   Guo, Gongde ^a   Wang, Hui ^b   Wei, Xin ^a  

^a FUJIAN NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

Binary classification; Dimensionality reduction; LDA for over reducing problem; Linear discriminant analysis

Indexed keywords

DISCRIMINANT ANALYSIS; IMAGE RETRIEVAL; LINEAR TRANSFORMATIONS; METADATA;

BINARY CLASSIFICATION; CLASSIFICATION PERFORMANCE; DIMENSIONALITY REDUCTION; FEATURE EXTRACTION METHODS; LDA FOR OVER-REDUCING PROBLEM; LINEAR DIMENSIONALITY REDUCTION; LINEAR DISCRIMINANT ANALYSES (LDA); LINEAR DISCRIMINANT ANALYSIS;

FACE RECOGNITION;

EID: 84984637792     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-19941-2_7     Document Type: Conference Paper

References (27)

1. Chen, L.-F., Liao, H.-Y.M., Ko, M.-T., Lin, J.-C., Yu, G.-J.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognit. 33(10), 1713–1726 (2000)
2. Zhao, X., Evans, N., Dugelay, J.: Semi-supervised face recognition with LDA selftraining. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 3041–3044. IEEE (2011)
3. He, C.L., Lam, L., Suen, C.Y.: Rejection measurement based on linear discriminant analysis for document recognition. Int. J. Doc. Anal. Recognit. (IJDAR) 14(3), 263–272 (2011)
4. Swets, D.L., Weng, J.J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 831–836 (1996)
5. He, X., Cai, D., Han, J.: Learning a maximum margin subspace for image retrieval. IEEE Trans. Knowl. Data Eng. 20(2), 189–201 (2008)
6. Martínez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)
7. Guo, Y., Hastie, T., Tibshirani, R.: Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8(1), 86–100 (2007)
8. Sharma, A., Paliwal, K.K.: A new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices. Pattern Recognit. 45(6), 2205–2213 (2012)
9. Sharma, A., Paliwal, K.K., Imoto, S., Miyano, S.: A feature selection method using improved regularized linear discriminant analysis. Mach. Vis. Appl. 25(3), 775–786 (2014)
10. Shu, X., Lu, H.: Linear discriminant analysis with spectral regularization. Appl. Intel. 40(4), 724–731 (2014)
11. Ye, J., Ji, S.: Discriminant analysis for dimensionality reduction: An overview of recent developments. In: Boulgouris, N.V., Plataniotis, K.N., Micheli-Tzanakou, E. (eds.) Biometrics: Theory, Methods, and Applications. Wiley-IEEE Press, New York (2010)
12. Fan, Z., Xu, Y., Zhang, D.: Local linear discriminant analysis framework using sample neighbors. IEEE Trans. Neural Netw. 22(7), 1119–1132 (2011)
13. Yao, C., Lu, Z., Li, J., Xu, Y., Han, J.: A subset method for improving linear discriminant analysis. Neurocomputing 138, 310–315 (2014)
14. Li, Z., Lin, D., Tang, X.: Nonparametric discriminant analysis for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 755–761 (2009)
15. Clemmensen, L., Hastie, T., Witten, D., Ersboll, B.: Sparse discriminant analysis. Technometrics 53(4), 406–413 (2011)
16. Zhao, M., Zhang, Z., Chow, T.W., Li, B.: A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction. Neural Netw. 55, 83–97 (2014)
17. Pang, S., Ozawa, S., Kasabov, N.: Incremental linear discriminant analysis for classification of data streams. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 35(5), 905–914 (2005)
18. Li, M., Yuan, B.: 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognit. Lett. 26(5), 527–532 (2005)
19. Nie, F., Xiang, S., Song, Y., Zhang, C.: Extracting the optimal dimensionality for local tensor discriminant analysis. Pattern Recognit. 42(1), 105–114 (2009)
20. Webb, A.R.: Statistical Pattern Recognition. Wiley, New York (2003)
21. Ye, J.: Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J. Mach. Learn. Res. 6, 483–502 (2005)
22. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report, Technical Report 07–49, University of Massachusetts, Amherst (2007)
23. Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 94–101. IEEE (2010)
24. Wolf, L., Hassner, T., Taigman, Y.: Similarity scores based on background samples. In: Zha, H., Taniguchi, R., Maybank, S. (eds.) ACCV 2009, Part II. LNCS, vol. 5995, pp. 88–97. Springer, Heidelberg (2010)
25. Kan, M., Xu, D., Shan, S., Li, W., Chen, X.: Learning prototype hyperplanes for face verification in the wild. IEEE Trans. Image Process. 22(8), 3310–3316 (2013)
26. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
27. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)