On incremental semi-supervised discriminant analysis

Pattern Recognition

Volumn 52, Issue , 2016, Pages 135-147

  Dhamecha, Tejas Indulal ^a   Singh, Richa ^a   Vatsa, Mayank ^a  

^a INDRAPRASTHA INSTITUTE OF INFORMATION TECHNOLOGY   (India)

Author keywords

Discriminant analysis; Face recognition; Incremental learning; Semi supervised learning

Indexed keywords

DISCRIMINANT ANALYSIS; MATRIX ALGEBRA; SUPERVISED LEARNING;

BETWEEN CLASS SCATTER; INCREMENTAL LEARNING; INCREMENTAL MODELING; LINEAR DISCRIMINANT ANALYSIS; MANIFOLD REGULARIZATIONS; PATTERN CLASSIFICATION PROBLEMS; SEMI- SUPERVISED LEARNING; SEMI-SUPERVISED DISCRIMINANT ANALYSIS;

FACE RECOGNITION;

EID: 84951849392     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2015.09.030     Document Type: Article

References (50)

1. R.A. Fisher The use of multiple measurements in taxonomic problems Ann. Eugen. 7 2 1936 179 188
2. D.L. Swets, and J.J. Weng Using discriminant eigenfeatures for image retrieval IEEE Trans. Pattern Anal. Mach. Intell. 18 8 1996 831 836
3. P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman Eigenfaces vs. Fisherfaces: recognition using class specific linear projection IEEE Trans. Pattern Anal. Mach. Intell. 19 7 1997 711 720
4. K. Fukunaga Introduction to Statistical Pattern Recognition 1990 Academic Press
5. D. Cai, X. He, J. Han, and H.-J. Zhang Orthogonal Laplacianfaces for face recognition IEEE Trans. Image Process. 15 11 2006 3608 3614
6. H.-T. Chen, H.-W. Chang, T.-L. Liu, Local discriminant embedding and its variants, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, 2005, pp. 846-853.
7. X. He, S. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang Face recognition using Laplacianfaces IEEE Trans. Pattern Anal. Mach. Intell. 27 3 2005 328 340
8. M. Zhu, and A.M. Martinez Subclass discriminant analysis IEEE Trans. Pattern Anal. Mach. Intell. 28 8 2006 1274 1286
9. S. Mika, G. Ratsch, J. Weston, B. Scholkopf, K. Mullers, Fisher discriminant analysis with kernels, in: Proceedings of IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing, 1999, pp. 41-48.
10. H. Li, T. Jiang, and K. Zhang Efficient and robust feature extraction by maximum margin criterion IEEE Trans. Neural Netw. 17 1 2006 157 165
11. J. Gui, Z. Sun, W. Jia, R. Hu, Y. Lei, and S. Ji Discriminant sparse neighborhood preserving embedding for face recognition Pattern Recognit. 45 8 2012 2884 2893
12. D. Cai, X. He, J. Han, Semi-supervised discriminant analysis, in: Proceedings of IEEE International Conference on Computer Vision, 2007, pp. 1-7.
13. M. Sugiyama, T. Idé, S. Nakajima, and J. Sese Semi-supervised local fisher discriminant analysis for dimensionality reduction Mach. Learn. 78 1-2 2010 35 61
14. Y. Zhang, and D.-Y. Yeung Semisupervised generalized discriminant analysis IEEE Trans. Neural Netw. 22 8 2011 1207 1217
15. B. Byun, On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling (Ph.D. thesis), Georgia Institute of Technology, 2012.
16. X. Zhu, and A.B. Goldberg Introduction to semi-supervised learning Synth. Lect. Artif. Intell. Mach. Learn. 3 1 2009 1 130
17. X. Wu, Y. Jia, and W. Liang Incremental discriminant-analysis of canonical correlations for action recognition Pattern Recognit. 43 12 2010 4190 4197
18. J. Gui, S.-L. Wang, and Y.-K. Lei Multi-step dimensionality reduction and semi-supervised graph-based tumor classification using gene expression data Artif. Intell. Med. 50 3 2010 181 191
19. P. Hall, D. Marshall, and R. Martin Merging and splitting eigenspace models IEEE Trans. Pattern Anal. Mach. Intell. 22 9 2000 1042 1049
20. S. Pang, S. Ozawa, and N. Kasabov Incremental linear discriminant analysis for classification of data streams IEEE Trans. Syst. Man Cybern. Part B: Cybern. 35 5 2005 905 914
21. H. Zhao, and P.C. Yuen Incremental linear discriminant analysis for face recognition IEEE Trans. Syst. Man Cybern. Part B: Cybern. 38 1 2008 210 221
22. L.-P. Liu, Y. Jiang, Z.-H. Zhou, Least square incremental linear discriminant analysis, in: Proceedings of IEEE International Conference on Data Mining, 2009, pp. 298-306.
23. T.-K. Kim, S.-F. Wong, B. Stenger, J. Kittler, R. Cipolla, Incremental linear discriminant analysis using sufficient spanning set approximations, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1-8.
24. T.-K. Kim, B. Stenger, J. Kittler, and R. Cipolla Incremental linear discriminant analysis using sufficient spanning sets and its applications Int. J. Comput. Vis. 91 2 2011 216 232
25. G.-F. Lu, J. Zou, and Y. Wang Incremental learning of complete linear discriminant analysis for face recognition Knowl.-Based Syst. 31 2012 19 27
26. G.-F. Lu, J. Zou, and Y. Wang Incremental complete LDA for face recognition Pattern Recognit. 45 7 2012 2510 2521
27. H. Lamba, T. I. Dhamecha, M. Vatsa, R. Singh, Incremental subclass discriminant analysis: a case study in face recognition, in: Proceedings of IEEE International Conference on Image Processing, 2012, pp. 593-596.
28. A. Joseph, Y.-M. Jang, S. Ozawa, and M. Lee Extension of incremental linear discriminant analysis to online feature extraction under nonstationary environments T. Huang, Z. Zeng, C. Li, C. Leung, Neural Information Processing, Lecture Notes in Computer Science vol. 7664 2012 Springer Berlin Heidelberg 640 647
29. Y.-R. Yeh, and Y.-C.F. Wang A rank-one update method for least squares linear discriminant analysis with concept drift Pattern Recognit. 46 5 2013 1267 1276
30. G.-F. Lu, Z. Jian, and Y. Wang Incremental learning from chunk data for IDR/QR Image Vis. Comput. 36 2015 1 8
31. J. Ye, Q. Li, H. Xiong, H. Park, R. Janardan, and V. Kumar Idr/qr: an incremental dimension reduction algorithm via qr decomposition IEEE Trans. Knowl. Data Eng. 17 9 2005 1208 1222
32. D. Chu, L.-Z. Liao, M.-P. Ng, X. Wang, Incremental linear discriminant analysis: a fast algorithm and comparisons, IEEE Trans. Neural Netw. Learn. Syst., http://dx.doi.org/10.1109/TNNLS.2015.2391201.
33. T. Hastie, R. Tibshirani, J.J.H. Friedman, The Elements of Statistical Learning, vol. 1, Springer, 2001.
34. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: Proceedings of Neural Information Processing Systems, vol. 14, 2001, pp. 585-591.
35. M. Belkin, and P. Niyogi Laplacian eigenmaps for dimensionality reduction and data representation Neural Comput. 15 6 2003 1373 1396
36. X. Niyogi, Locality preserving projections, in: Proceedings of Neural Information Processing Systems, vol. 16, 2004, p. 153.
37. I. Jolliffe Principal Component Analysis 2005 Wiley
38. X. He, Incremental semi-supervised subspace learning for image retrieval, in: Proceedings of International Conference on Multimedia, 2004, pp. 2-8.
39. Y. Song, F. Nie, and C. Zhang Semi-supervised sub-manifold discriminant analysis Pattern Recognit. Lett. 29 13 2008 1806 1813
40. B. Geng, D. Tao, C. Xu, L. Yang, and X.-S. Hua Ensemble manifold regularization IEEE Trans. Pattern Anal. Mach. Intell. 34 6 2012 1227 1233
41. J. Gui, W. Jia, L. Zhu, S.-L. Wang, and D.-S. Huang Locality preserving discriminant projections for face and palmprint recognition Neurocomputing 73 13 2010 2696 2707
42. T. Zhou, and D. Tao Double shrinking sparse dimension reduction IEEE Trans. Image Process. 22 1 2013 244 257
43. L.N. Trefethen, D. Bau III, Numerical Linear Algebra, Society for Industrial and Applied Mathematics, Philadelphia, 1997.
44. M. Turk, and A. Pentland Eigenfaces for recognition J. Cogn. Neurosci. 3 1 1991 71 86
45. T. Sim, S. Baker, M. Bsat, The CMU pose, illumination, and expression (PIE) database, in: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, 2002, pp. 46-51.
46. S.Z. Li, D. Yi, Z. Lei, S. Liao, The CASIA NIR-VIS 2.0 face database, in: Proceedings of IEEE CVPR Workshop on Perception Beyond the Visible Spectrum, 2013, pp. 1-6.
47. R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker Multi-PIE Image Vis. Comput. 28 5 2010 807 813
48. J.H. Friedman Regularized discriminant analysis J. Am. Stat. Assoc. 84 405 1989 165 175
49. X.-Y. Zhang, and C.-L. Liu Evaluation of weighted Fisher criteria for large category dimensionality reduction in application to Chinese handwriting recognition Pattern Recognit. 46 9 2013 2599 2611
50. Ye, Jieping and Janardan, Ravi and Park, Cheong Hee and Park, Haesun, An optimization criterion for generalized discriminant analysis on undersampled problems, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (8) (2004) 982-994