Sparse neighbor representation for classification

Pattern Recognition Letters

Volumn 33, Issue 5, 2012, Pages 661-669

  Hui, Kang Hua ^a   Li, Chun Li ^a   Zhang, Lei ^a  

^a CIVIL AVIATION UNIVERSITY OF CHINA   (China)

Author keywords

K nearest neighbors; Locally linear embedding; Sparse neighbor representation; Sparse representation

Indexed keywords

CLASSIFICATION TASKS; DATA SETS; DIMENSIONALITY REDUCTION METHOD; EMBEDDINGS; K-NEAREST NEIGHBORS; LOCALLY LINEAR EMBEDDING; LOW DIMENSIONAL; NONLINEAR DATA SET; RECONSTRUCTION PROPERTY; SPARSE NEIGHBOR REPRESENTATION; SPARSE REPRESENTATION; SPARSE SIGNAL REPRESENTATION;

PATTERN RECOGNITION; SOFTWARE ENGINEERING;

FACE RECOGNITION;

EID: 84856516193     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.11.010     Document Type: Article

References (40)

1. M. Aharon, M. Elad, and A. Bruckstein K-svd: An algorithm for designing overcomplete dictionaries for sparse representation IEEE Trans. Signal Process. 54 2006 4311 4321
2. P. Belhumeur, and D. Kriegman What is the set of images of an object under all possible illumination conditions? Int. J. Computer Vis. 28 3 1998 245 260 (Pubitemid 128522672)
3. P. Belhumeur, J. Hespanda, and D. Kriegman Eigenfaces versus fisherfaces: Recognition using class specific linear projection IEEE Trans. Pattern Anal. Machine Intell. 19 7 1997 711 720 (Pubitemid 127762263)
4. S. Chen, D.L. Donoho, and M.A. Saunders Atomic decomposition by basis pursuit SIAM J. Sci. Comput. 20 1 1998 33 61 (Pubitemid 128689501)
5. Chen, Y.; Do, T.T.; Tran, T.D.; 2010. Robust face recognition using locally adaptive sparse representation. In: IEEE 17th International Conference on Image Processing, pp. 1657-1660.
6. D. D Visualizing mercer kernel feature spaces via kernelized locally-linear embeddings Adv. Neural Inform. Process. Syst. 2001
7. D. D, and G. G Hessian eigenmaps: New locally linear embedding techniques for high-dimensional data Proc. Natl. Acad. Sci. 100 10 2003 5591 5596
8. M. Elad, and M. Aharon Image denoising via sparse and redundant representation over learned dictionaries IEEE Trans. Image Process. 15 12 2006 3736 3744
9. Z. Fan, Y. Xu, and D. Zhang Local linear discriminant analysis framework using sample neighbors IEEE Trans. Neural Networks 22 7 2011 1119 1132
10. R.A. Fisher The use of multiple measures in taxonomic problems Ann. Eugenics 7 1936 179 188
11. Fix, E.; Hodges, J.; 1951. Discriminatory analysis, nonparametric discrimination: Consistency properties, technical Report TR4, US Air Force School of Aviation Medicine, Randolph Field, Tex.
12. A. Georghiades, D. Kriegman, and P. Belhumeur Illumination cones for recognition under variable lighting: Faces IEEE Int. Conf. Computer Vis. Pattern Recognit. 1998 52 59
13. A. Georghiades, P. Belhumeur, and D. Kriegman From few to many: Generative models for recognition under variable pose and illumination Proc. IEEE Int. Conf. Automatic Face Gesture Recognit. 2000 277 284
14. Gross, R.; Matthews, I.; Cohn, J.; Kanade, T.; Baker, S.; 2008. Multi-pie. In: Proc IEEE Conference on Face and Gesture Recognition.
15. X. He, and P. Niyogi Locality preserving projections Adv. Neural Inform. Process. Syst. 2003
16. K. Huang, and S. Aviyente Sparse representation for signal classification Adv. Neural Inform. Process. Syst. 19 2007 609 616
17. I.T. Jolliffe Principal Component Analysis 1989 Springer NY
18. P. Kang, and S. Cho Locally linear reconstruction for instance-based learning Pattern Recognition 41 2008 3507 3518
19. E. Kokiopoulou, and Y. Saad Orthogonal neighborhood preserving projections: A projection-based dimensionality reduction technique IEEE Trans. Pattern Anal. Machine Intell. 29 12 2007 2143 2156 (Pubitemid 350124063)
20. Kouropteva, O.; Okun, O.; Pietikinen, M.; 2002. Selection of the optimal parameter value for the locally linear embedding algorithm. In: Proc. of the 1st International Conference on Fuzzy Systems and Knowledge Discovery, Singapore, pp. 359-363.
21. K.-C. Lee, J. Ho, and D. Kriegman Acquiring linear subspaces for face recognition under variable lighting IEEE Trans. Pattern Anal. Machine Intell. 27 5 2005 684 698 (Pubitemid 40608402)
22. H. Lee, A. Battle, R. Raina, and A.Y. Ng Efficient sparse coding algorithms Adv. Neural Inform. Process. Syst. 2006
23. Li, C.G.; Guo, J.; Zhang, H.G.; 2010. Local sparse representation based classification. In: 20th International Conference on Pattern Recognition, pp. 649-652.
24. Z.Y. Liu, K.C. Chiu, and L. Xu Improved system for object detection and star/galaxy classification via local subspace analysis Neural Networks 16 3-4 2003 437 451 (Pubitemid 36379095)
25. J. Mairal, M. Elad, and G. Sapiro Sparse representation for color image restoration IEEE Trans. Image Process. 17 1 2008 53 69
26. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman Discriminative learned dictionaries for local image analysis IEEE Int. Conf. Computer Vis. Pattern Recognit. 2008 1 8
27. A. Majumdar, and R. Ward Improved group sparse classifier Pattern Recognition Lett. 31 2010 1959 1964
28. S. Mallat, and Z. Zhong Matching pursuit in a time-frequency dictionary IEEE Trans. Signal Process. 41 1992 617 643
29. Martinez, A.; Benavente, R.; 1998. The ar face database, cVC Technical Report 24.
30. Z. Nan, and Y. Jian K nearest neighbor based local sparse representation classifier Chinese Conf. Pattern Recognit. 2010
31. B. Olshausen, P. Sallee, and M. Lewicki Learning sparse multiscale image representations Adv. Neural Inform. Process. Syst. 15 2003 1327 1334
32. Pati, Y.; Rezaiifar, R.; Krishnaprasad, P.; 1993. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In: The 27th Asilomar Conference on Signals, Systems and Computers, vol. 1. pp. 40-44.
33. I. Ramirez, P. Sprechmann, and G. Sapiro Classification and clustering via dictionary learning with structured incoherence and shared features IEEE Int. Conf. Computer Vis. Pattern Recognit. 2010 3501 3508
34. S. Roth, and M.J. Black Fields of experts: A framework for learning image priors IEEE Int. Conf. Computer Vis. Pattern Recognit. 2 2005 860 867
35. S.T. Roweis, and L.K. Saul Nonlinear dimensionality reduction by locally linear embedding Science 290 2000 2323 2326 (Pubitemid 32041578)
36. K. Sung, and T. Poggio Example-based learning for viewbased face detection IEEE Trans. Pattern Anal. Machine Intell. 20 1998 39 51 (Pubitemid 128741306)
37. J. Wright, A. Yang, S. Sastry, and Y. Ma Robust face recognition via sparse representation IEEE Trans. Pattern Anal. Machine Intell. 31 2 2009 210 227
38. Wright, J.; Ma, Y.; J. M. et al.; 2009. Sparse representation for computer vision and pattern recognition. IEEE Int. Conf. Computer Vis. Pattern Recognit.
39. Y. Xu, D. Zhang, J. Yang, and J. Yang A two-phase test sample sparse representation method for use with face recognition IEEE Trans. Circuits Syst. Video Technol. 2011 doi:10.1109/TCSVT.2011.213879
40. M. Yang, L. Zhang, J. Yang, and D. Zhang Robust sparse coding for face recognition IEEE Int. Conf. Computer Vis. Pattern Recognit. 2011