Batch mode sparse active learning

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 875-882

  Shi, Lixin ^a   Zhao, Yuhang ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Active learning; Batch mode sparse active learning; Sparse classification; Submodularity

Indexed keywords

ACTIVE LEARNING; BATCH MODES; BOUNDED ALGORITHMS; CLASSIFICATION METHODS; DATA SETS; EMPIRICAL TEST; LABELED TRAINING DATA; NON-LINEAR; SPARSE CLASSIFICATION; SPARSE REPRESENTATION; STATE-OF-THE-ART METHODS; SUBMODULARITY; UNIFIED FRAMEWORK;

ALGORITHMS;

DATA MINING;

EID: 79951741424     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2010.175     Document Type: Conference Paper

References (35)

1. M. Aharon, M. Elad, and A. Bruckstein. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11):4311 - 4322, 2006. (Pubitemid 44637761)
2. E. Amaldi and V. Kann. On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theoretical Computer Science, 209:237 - 260, 1998. (Pubitemid 128450440)
3. R. Basri, T. Hassner, and L. Zelnik-Manor. Approximate nearest subspace search with applications to pattern recognition. In CVRP'07, 2007.
4. R. Basri and D. W. Jacobs. Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell., 25(2):218-233, 2003.
5. E. Candès, M. Rudelson, T. Tao, and R. Vershynin. Error correction via linear programming. In FOCS'05, pages 295-308, 2005.
6. E. J. Candes and J. Romberg. Quantitative robust uncertainty principles and optimally sparse decompositions. Found. Comput. Math., 6(2):227-254, 2006. (Pubitemid 43824483)
7. E. J. Candès and T. Tao. Decoding by linear programming. IEEE Transactions on Information Theory, 51(12):4203 - 4215, 2005. (Pubitemid 41800353)
8. E. J. Candès and M. Wakin. An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2):21 - 30, 2008.
9. S. S. Chen, D. L. Donoho, and M. A. Saunders. Atomic decomposition by basis pursuit. SIAM Rev., 43(1):129-159, 2001. (Pubitemid 32406896)
10. D. L. Donoho. For most large underdetermined systems of linear equations the minimal ℓ1-norm solution is also the sparsest solution. Comm. Pure and Applied Math, 59(6):797 - 829, 2006. (Pubitemid 43667226)
11. D. L. Donoho and M. Elad. Optimally sparse representation in general(non-orthogonal) dictionaries via ℓ1 minimization. In PNAS'03, pages 2197 - 2202, 2003. (Pubitemid 36297476)
12. D. L. Donoho, Y. Tsaig, I. Drori, and J. Starck. Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit. Technical Report 2006-02, Standford, Department of Statistics, 2006.
13. A. Frank and A. Asuncion. UCI machine learning repository, 2010.
14. J. F. Gemmeke and B. Cranen. Noise reduction through compressed sensing. In Proceedings of Interspeech, pages 1785 - 1788, 2008.
15. J. F. Gemmeke and T. Virtanen. Noise robust digit recognition using sparse representations. In ISCA 2008 ITRW "Speech Analysis and Processing for knowledge discovery", 2008.
16. S. C. H. Hoi, R. Jin, and M. R. Lyu. Large-scale text categorization by batch mode active learning. In WWW'06, pages 633-642, 2006. (Pubitemid 46946655)
17. S. C. H. Hoi, R. Jin, J. Zhu, and M. R. Lyu. Semisupervised svm batch mode active learning with applications to image retrieval. ACM Trans. Inf. Syst., 27(3):1-29, 2009.
18. P. Indyk. Explicit constructions for compressed sensing of sparse signals. In SODA'08, pages 30-33, 2008.
19. J. Wright K. Min, Z. Zhang and Y. Ma. Decomposing background topics from keywords by principal component pursuit. In CIKM'10, 2010.
20. A. Krause and V. Cevher. Submodular dictionary selection for sparse representation. In ICML'10, 2010.
21. A. Krause, A. Singh, and C. Guestrin. Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies. J. Mach. Learn. Res., 9:235-284, 2008. (Pubitemid 351469021)
22. J. Ma and F. L. Dimet. Deblurring from highly incomplete measurements for remote sensing. IEEE Trans. Geoscience and Remote Sensing, 47(3):792 - 802, 2009.
23. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Discriminative learned dictionaries for local image analysis. In CVPR'08, 2008.
24. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning. In NIPS'08, pages 1033-1040, 2008.
25. G. Nemhauser, L. A. Wolsey, and M. L. Fischer. An analysis of the approximations for maximizing submodular set functions. Mathematical Programming, 14, 1978.
26. P. Sen, G. M. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3):93-106, 2008.
27. G. Shakhnarovich, T. Darrell, and P. Indyk. Nearest-neighbor methods in learning and vision. IEEE Transactions on Neural Networks, 19(2):377-377, 2008.
28. T. R. Shultz and J. L. Elman. Analyzing cross-connected networks. In NIPS'93, pages 1117-1124, 1993.
29. Z. Si and Y. N. Wu. Wavelet, active basis, and shape script: a tour in the sparse land. In MIR'10, pages 201-210, 2010.
30. J. A. Tropp and A. C. Gilbert. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53(12):4655-4666, 2007.
31. D. P. Wipf and B. D. Rao. Sparse bayesian learning for basis selection. IEEE Transactions on Signal Processing, 52(8):2153 - 2164, 2004.
32. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell., 31(2):210-227, 2009.
33. A. Y. Yang, J. Wright, Y. Ma, and S. S. Sastry. Feature selection in face recognition: A sparse representation perspective. Technical Report UCB/EECS-2007-99, University of California at Berkeley, 2007.
34. J. Yang and Y. Zhang. Alternating direction algorithms for ℓ1-problems in compressive sensing. Technical Report TR09-37, Rice University, 2009.
35. X. Zhu, J. Lafferty, and Z. Ghahramani. Combining active learning and semi-supervised learning using gaussian fields and harmonic functions. In ICML Workshop'03, pages 58 - 65, 2003.