Semi-supervised robust dictionary learning via efficient l-norms minimization

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 1145-1152

  Wang, Hua ^a   Nie, Feiping ^b   Cai, Weidong ^c   Huang, Heng ^a  

^a Department of Metallurgical and Materials Engineering   (United States)

^b UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

^c UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Dictionary Learning; Robust Dictionary Learning; Semi Supervised Learning

Indexed keywords

ALGORITHMS; STATISTICS; SUPERVISED LEARNING;

COMPUTER VISION APPLICATIONS; DICTIONARY LEARNING; LEARNED DICTIONARIES; LINEAR-DECOMPOSITION; OBJECTIVE FUNCTIONS; SEMI-SUPERVISED LEARNING; SPARSE REGULARIZATIONS; STATE-OF-THE-ART PERFORMANCE;

OPTIMIZATION;

EID: 84898828969     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.146     Document Type: Conference Paper

References (18)

1. M. Aharon, M. Elad, and A. Bruckstein. SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. IEEE Transactions on Signal Processing, 54(11):4311-4322, 2006.
2. A. Argyriou, T. Evgeniou, and M. Pontil. Multi-task feature learning. NIPS, pages 41-48, 2007.
3. S. Bengio, F. Pereira, Y. Singer, and D. Strelow. Group sparse coding. In NIPS, 2009.
4. X. Cai, F. Nie, H. Huang, and C. Ding. Multi-class l2,1-norm support vector machine. In ICDM, pages 91-100, 2011.
5. E. Cand̀es and T. Tao. Decoding by linear programming. IEEE Transactions on Information Theory, 51:4203-4215, 2005.
6. E. Cand̀es and M. WAKIN. An introduction to compressive sensing. IEEE Signal Processing Magazine, 25(2):21-30, 2008.
7. M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15(12):3736-3745, 2006.
8. H. Lee, A. Battle, R. Raina, and A. Ng. Efficient sparse coding algorithms. In NIPS, 2007.
9. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Discriminative learned dictionaries for local image analysis. In CVPR, pages 1-8, 2008.
10. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning. In NIPS, pages 1033-1040, 2009.
11. F. Nie, H. Huang, X. Cai, and C. Ding. Efficient and Robust Feature Selection via Joint l2,1-Norms Minimization. In NIPS, 2010.
12. G. Obozinski, B. Taskar, and M. Jordan. Multi-task feature selection. Technical report, Department of Statistics, University of California, Berkeley, 2006.
13. R. Raina, A. Battle, H. Lee, B. Packer, and A. Ng. Selftaught learning: Transfer learning from unlabeled data. In ICML, pages 759-766, 2007.
14. R. Tibshirani. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society. Series B (Methodological), pages 267-288, 1996.
15. H. Wang, F. Nie, and H. Huang. Robust and discriminative self-taught learning. In ICML, pages 298-306, 2013.
16. H. Wang, F. Nie, H. Huang, S. L. Risacher, C. Ding, A. J. Saykin, L. Shen, and ADNI. A new sparse multi-task regression and feature selection method to identify brain imaging predictors for memory performance. IEEE Conference on Computer Vision (ICCV), pages 557-562, 2011.
17. M. Zhang and Z. Zhou. Ml-knn: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7):2038-2048, 2007.
18. Q. Zhang and B. Li. Discriminative K-SVD for dictionary learning in face recognition. In CVPR, pages 2691-2698, 2010.