Sparse representation based projections

BMVC 2011 - Proceedings of the British Machine Vision Conference 2011

Volumn , Issue , 2011, Pages

  Timofte, Radu ^a   Van Gool, Luc ^a,b  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

DIMENSIONALITY REDUCTION; HANDWRITTEN DIGIT; NONLINEAR PROJECTIONS; POINT OF DEPARTURES; SPARSE REPRESENTATION;

COMPUTER VISION;

EID: 84898446609     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C25.61     Document Type: Conference Paper

References (28)

1. Muhammad Salman Asif. Primal dual pursuit: A homotopy based algorithm for the dantzig selector. M.S. Thesis. Georgia Institute of Technology, 2008.
2. Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in Neural Information Processing Systems 14, pages 585-591. MIT Press, 2001.
3. I. Borg and P. Groenen. Modern Multidimensional Scaling: theory and applications (2nd ed.). Springer-Verlag, 2005.
4. A.M. Bruckstein, D.L. Donoho, and M. Elad. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review, 51(1):34-81, February 2009.
5. Deng Cai. Spectral Regression: A Regression Framework for Efficient Regularized Sub-space Learning. PhD thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, May 2009.
6. Deng Cai, Xiaofei He, and Jiawei Han. Efficient kernel discriminant analysis via spectral regression. In Proc. Int. Conf. on Data Mining (ICDM'07), 2007.
7. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
8. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, 1995.
9. Fernando De la Torre. A least-squares framework for component analysis. under review in PAMI, 2011.
10. 1-norm minimization problems when the solution may be sparse. IEEE Transactions on Information Theory, 54(11): 4789-4812, 2008.
11. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res., 9:1871-1874, 2008.
12. R.A. Fisher. The statistical utilization of multiple measurements. Annals of Eugenics, 8:376-386, 1938.
13. J.H. Friedman. Regularized discriminant analysis. Journal of the American Statistical Association, 84:165-175, 1989.
14. Arvind Ganesh, Andrew Wagner, Zihan Zhou, Allen Yang, Yi Ma, and John Wright. Chapter 1. face recognition by sparse representation (accepted). Cambridge University Press, 2011.
15. Xiaofei He and Partha Niyogi. Locality preserving projections. In NIPS, 2003.
16. Shaoli Huang, Cheng Cai, and Yang Zhang. Dimensionality reduction by using sparse reconstruction embedding. In PCM (2), pages 167-178, 2010.
17. Subhransu Maji and Jitendra Malik. Fast and accurate digit classification. Technical Report UCB/EECS-2009-159, EECS Department, University of California, Berkeley, Nov 2009. URL http://www.eecs.berkeley.edu/Pubs/ TechRpts/2009/EECS-2009-159.html.
18. A.M. Martinez and A.C. Kak. PCA versus LDA. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(2):228-233, 2001.
19. Sam Roweis and Lawrence Saul. Nonlinear dimensionality reduction by locally linear embedding. In IEEE International Conference on Computer Vision, Volume 290, pages 2323-2326, 2001.
20. Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition. In submitted to International Joint Conference on Neural Networks, 2011.
21. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319-2323, december 2000.
22. Radu Timofte, Karel Zimmermann, and Luc Van Gool. Multi-view traffic sign detection, recognition, and 3d localisation. In Proceedings of the IEEE Computer Society's Workshop on Applications of Computer Vision (WACV), Snowbird, Utah, USA, December 2009. IEEE Computer Society.
23. John Wright, Allen Y. Yang, Arvind Ganesh, Shankar Sastry, and Yi Ma. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Learning, 31(2), February 2009.
24. Shuicheng Yan, Dong Xu, Benyu Zhang, Hong-Jiang Zhang, Qiang Yang, and S. Lin. Graph embedding and extensions: A general framework for dimensionality reduction. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(1):40-51, january 2007. ISSN 0162-8828. doi:10.1109/TPAMI.2007.250598.
25. 1-minimization algorithms and an application in robust face recognition: a review. Technical Report UCB/EECS-2010-13, University of California, Berkeley, 2010.
26. Tianhao Zhang, Dacheng Tao, Xuelong Li, and Jie Yang. Patch alignment for dimensionality reduction. IEEE Trans. Knowl. Data Eng., 21(9):1299-1313, 2009.
27. Tianyi Zhou, Dacheng Tao, and Xindong Wu. Manifold elastic net: a unified framework for sparse dimension reduction. Data Min. Knowl. Discov., 22(3):340-371, 2011.
28. Hui Zou, Trevor Hastie, and Rob Tibshirani. Sparse principal component analysis. JCGS, 15:262-286, 2006.