Active learning based on locally linear reconstruction

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 33, Issue 10, 2011, Pages 2026-2038

  Zhang, Lijun ^a   Chen, Chun ^a   Bu, Jiajun ^a   Cai, Deng ^b   He, Xiaofei ^b   Huang, Thomas S ^c  

^a Joint Center for Traditional Chinese Medicine Information and Digitalization   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Active learning; experimental design; local structure; reconstruction.

Indexed keywords

ACTIVE LEARNING; ACTIVE-LEARNING ALGORITHM; DATA POINTS; DATA SETS; DATA SPACE; EUCLIDEAN STRUCTURE; LINEAR COMBINATIONS; LINEAR RECONSTRUCTION; LOCAL MANIFOLD STRUCTURE; LOCAL STRUCTURE; OPTIMIZATION PROBLEMS; OPTIMIZATION SCHEME; OPTIMUM EXPERIMENTAL DESIGN; PARAMETER ESTIMATE; REPRESENTATIVE POINT; TRANSDUCTIVE LEARNING;

CONVEX OPTIMIZATION; DESIGN; OPTIMIZATION; PARAMETER ESTIMATION; STATISTICS;

LEARNING ALGORITHMS;

EID: 80051991360     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2011.20     Document Type: Article

References (41)

1. X. Zhu, "Semi-Supervised Learning Literature Survey," Technical Report 1530, Dept. of Computer Sciences, Univ. of Wisconsin-Madison, 2005.
2. D. Zhou, O. Bousquet, T.N. Lal, J. Weston, and B. Schölkopf, "Learning with Local and Global Consistency," Advances in Neural Information Processing Systems, vol. 16, pp. 321-328, MIT Press, 2004.
3. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold Regulariza-tion: A Geometric Framework for Learning from Labeled and Unlabeled Examples," J. Machine Learning Research, vol. 7, pp. 2399-2434, 2006. (Pubitemid 44708005)
4. D.A. Cohn, Z. Ghahramani, and M.I. Jordan, "Active Learning with Statistical Models," J. Artificial Intelligence Research, vol. 4, pp. 129-145, 1996. (Pubitemid 126646151)
5. B. Settles, "Active Learning Literature Survey," Computer Sciences Technical Report 1648, Univ. of Wisconsin-Madison, 2009.
6. D.D. Lewis and W.A. Gale, "A Sequential Algorithm for Training Text Classifiers," Proc. ACM SIGIR, pp. 3-12, 1994.
7. S. Tong and D. Koller, "Support Vector Machine Active Learning with Applications to Text Classification," J. Machine Learning Research, vol. 2, pp. 45-66, 2002.
8. M. Lindenbaum, S. Markovitch, and D. Rusakov, "Selective Sampling for Nearest Neighbor Classifiers," Machine Learning, vol. 54, no. 2, pp. 125-152, 2004.
9. A. Fujii, T. Tokunaga, K. Inui, and H. Tanaka, "Selective Sampling for Example-Based Word Sense Disambiguation," Computational Linguistics, vol. 24, no. 4, pp. 573-597, 1998. (Pubitemid 128455525)
10. H.S. Seung, M. Opper, and H. Sompolinsky, "Query by Committee," Proc. Fifth Ann. Workshop Computational Learning Theory, pp. 287-294, 1992. (Pubitemid 23615454)
11. P. Melville and R.J. Mooney, "Diverse Ensembles for Active Learning," Proc. 21st Int'l Conf. Machine learning, 2004.
12. N. Roy and A. McCallum, "Toward Optimal Active Learning through Sampling Estimation of Error Reduction," Proc. 18th Int'l Conf. Machine Learning, pp. 441-448, 2001.
13. X. Zhu, J. Lafferty, and Z. Ghahramani, "Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions," Proc. Int'l Conf. Machine Learning Workshop Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, pp. 58-65, 2003.
14. A. Atkinson, A. Donev, and R. Tobias, Optimum Experimental Designs, with SAS. Oxford Univ. Press, 2007.
15. X. He, W. Min, D. Cai, and K. Zhou, "Laplacian Optimal Design for Image Retrieval," Proc. ACM SIGIR, pp. 119-126, 2007. (Pubitemid 350164952)
16. K. Yu, J. Bi, and V. Tresp, "Active Learning via Transductive Experimental Design," Proc. 23rd Int'l Conf. Machine Learning, pp. 1081-1088, 2006.
17. L. Zhang, C. Chen, W. Chen, J. Bu, D. Cai, and X. He, "Convex Experimental Design Using Manifold Structure for Image Retrieval," Proc. 17th ACM Int'l Conf. Multimedia, pp. 45-53, 2009.
18. S.T. Roweis and L.K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding," Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 2000. (Pubitemid 32041578)
19. J. Tenenbaum, V. de Silva, and J. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction," Science, vol. 290, no. 5500, pp. 2319-2323, 2000. (Pubitemid 32041577)
20. M. Belkin and P. Niyogi, "Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering," Advances in Neural Information Processing Systems, vol. 14, pp. 585-591, MIT Press, 2002.
21. X. He and P. Niyogi, "Locality Preserving Projections," Advances in Neural Information Processing Systems, vol. 16, pp. 153-160, MIT Press, 2004.
22. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer, 2009.
23. S.P. Asprey and S. Macchietto, "Designing Robust Optimal Dynamic Experiments," J. Process Control, vol. 12, no. 4, pp. 545-556, 2002. (Pubitemid 34302548)
24. R.H. Hardin and N.J.A. Sloane, "A New Approach to the Construction of Optimal Designs," J. Statistical Planning and Inference, vol. 37, no. 3, pp. 339-369, 1993.
25. S.B. Grary and C. Spera, "Optimal Experimental Design for Combinatorial Problems," Computational Economics, vol. 9, no. 3, pp. 241-255, Aug. 1996. (Pubitemid 126711379)
26. X. Li and Y. Pang, "Deterministic Column-Based Matrix Decomposition," IEEE Trans. Knowledge and Data Eng., vol. 22, no. 1, pp. 145-149, Jan. 2010.
27. C. Chen, L. Zhang, J. Bu, C. Wang, and W. Chen, "Constrained Laplacian Eigenmap for Dimensionality Reduction," Neurocom-puting, vol. 73, nos. 4-6, pp. 951-958, 2010.
28. M. Belkin, I. Matveeva, and P. Niyogi, "Regularization and Semi-Supervised Learning on Large Graphs," Proc. 17th Ann. Conf. Computational Learning Theory, pp. 624-638, 2004. (Pubitemid 38940364)
29. X. Zhu, Z. Ghahramani, and J.D. Lafferty, "Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions," Proc. 20th Int'l Conf. Machine Learning, pp. 912-919, 2003.
30. C.J.C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998. (Pubitemid 128695475)
31. Y. Chen, T.A. Davis, W.W. Hager, and S. Rajamanickam, "Algorithm 887: Cholmod, Supernodal Sparse Cholesky Factorization and Update/Downdate, " ACM Trans. Math. Software, vol. 35, no. 3, pp. 1-14, 2008.
32. G.H. Golub and C.F. Van Loan, Matrix Computations, third ed. Johns Hopkins Univ. Press, 1996.
33. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, 2004.
34. C.-C. Chang and C.-J. Lin LIBSVM: A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.
35. M. Grant and S. Boyd, "CVX: Matlab Software for Disciplined Convex Programming, Version 1.21," http://cvxr.com/cvx, Oct. 2010.
36. M. Grant and S. Boyd, "Graph Implementations for Non-smooth Convex Programs," Recent Advances in Learning and Control, V. Blondel, S. Boyd, and H. Kimura, eds., vol. 371, pp. 95-110, Springer, 2008.
37. H. Yu, M. Li, H. Jiang Zhang, and J. Feng, "Color Texture Moments for Content-Based Image Retrieval," Proc. Int'l Conf. Image Processing, pp. 24-28, 2002.
38. M. Muja and D.G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration," Proc. Int'l Conf. Computer Vision Theory and Application, pp. 331-340, 2009.
39. S. Arya, D.M. Mount, N.S. Netanyahu, R. Silverman, and A.Y. Wu, "An Optimal Algorithm for Approximate Nearest Neighbor Searching Fixed Dimensions," J. ACM, vol. 45, no. 6, pp. 891-923, 1998.
40. H. Lee, A. Battle, R. Raina, and A.Y. Ng, "Efficient Sparse Coding Algorithms," Advances in Neural Information Processing Systems, vol. 19, pp. 801-808, MIT Press, 2007.
41. J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma, "Robust Face Recognition via Sparse Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009.