Discriminative concept factorization for data representation

Neurocomputing

Volumn 74, Issue 18, 2011, Pages 3800-3807

  Hua, Wei ^a   He, Xiaofei ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Concept factorization; Dimensionality reduction; Semi supervised learning

Indexed keywords

CLASSIFICATION TASKS; CONVEX PROGRAMMING; DATA RECONSTRUCTION; DATA REPRESENTATIONS; DIMENSIONALITY REDUCTION; FACTORIZATION TECHNIQUES; FEATURE SPACE; ITERATIVE ALGORITHM; LABEL INFORMATION; LOW-DIMENSIONAL REPRESENTATION; NONNEGATIVE MATRIX FACTORIZATION; OPTIMIZATION PROBLEMS; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING;

BLIND SOURCE SEPARATION; MATRIX ALGEBRA; SUPERVISED LEARNING;

FACTORIZATION;

ARTICLE; CLASSIFICATION; CLASSIFIER; COMPUTER PROGRAM; DATA ANALYSIS; DATA MINING; DATA SYNTHESIS; DISCRIMINATIVE CONCEPT FACTORIZATION; INFORMATION PROCESSING; INFORMATION RETRIEVAL; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL;

EID: 80053321199     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.07.020     Document Type: Article

References (33)

1. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2000, Wiley-Interscience Publication.
2. I.K. Fodor, A Survey of Dimension Reduction Techniques, Technical Report, Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, 2002.
3. Cai D., He X., Han J. Srda: an efficient algorithm for large-scale discriminant analysis. IEEE Transactions on Knowledge and Data Engineering 2008, 20:1-12.
4. L. Zhang, C. Chen, J. Bu, Z. Chen, S. Tan, X. He, Discriminative codeword selection for image representation, in: Proceedings of the 18th ACM International Conference on Multimedia, 2010, pp. 173-182.
5. D. Xu, S. Yan, L. Zhang, H.-J. Zhang, Z. Liu, H.-Y. Shum, Concurrent subspaces analysis, in: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005, pp. 203-208.
6. Xu D., Yan S. Semi-supervised bilinear subspace learning. IEEE Transactions on Image Processing 2009, 18:1671-1676.
7. Tao D., Li X., Wu X., Maybank S. Tensor rank one discriminant analysis-a convergent method for discriminative multilinear subspace selection. Neurocomputing 2008, 71:1866-1882.
8. Li X., Pang Y., Yuan Y. L1-norm-based 2DPCA. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 2010, 40:1170-1175.
9. Tao D., Li X., Wu X., Maybank S.J. Geometric mean for subspace selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009, 31:260-274.
10. Li X., Lin S., Yan S., Xu D. Discriminant locally linear embedding with high-order tensor data. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 2008, 38:342-352.
11. Strang G. Introduction to Linear Algebra 2003, Wellesley-Cambridge Press. 3rd ed.
12. Bishop C.M. Pattern Recognition and Machine Learning (Information Science and Statistics) 2007, Springer.
13. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
14. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: Advances in Neural Information Processing Systems, vol. 14, 2001, pp. 585-591.
15. X. He, P. Niyogi, Locality preserving projections, in: Advances in Neural Information Processing Systems, vol. 16, 2003, pp. 153-160.
16. Chen C., Zhang L., Bu J., Wang C., Chen W. Constrained Laplacian eigenmap for dimensionality reduction. Neurocomputing 2010, 73:951-958.
17. He X., Yan S., Hu Y., Niyogi P., Zhang H.-J. Face recognition using laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005, 27:328-340.
18. Lee D.D., Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401:788-791.
19. W. Xu, X. Liu, Y. Gong, Document clustering based on non-negative matrix factorization, in: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2003, pp. 267-273.
20. S.Z. Li, X. Hou, H. Zhang, Q. Cheng, Learning spatially localized, parts-based representation, in: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. 207-212.
21. Brunet J.-P., Tamayo P., Golub T.R., Mesirov J.P. Metagenes and molecular pattern discovery using matrix factorization. Proceedings of the National Academy of Sciences of the United States of America 2004, 101:4164-4169.
22. W. Xu, Y. Gong, Document clustering by concept factorization, in: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004, pp. 202-209.
23. Mairal J., Bach F., Ponce J., Sapiro G., Zisserman A. Supervised dictionary learning. Advances in Neural Information Processing Systems 2009, vol. 21:1033-1040.
24. D.D. Lee, H.S. Seung, Algorithms for non-negative matrix factorization, in: Advances in Neural Information Processing Systems, vol. 13, 2000, pp. 556-562.
25. C. Wang, S. Yan, L. Zhang, H. Zhang, Non-negative semi-supervised learning, in: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, vol. 5, Clearwater Beach, Florida USA, pp. 575-582.
26. Lee H., Yoo J., Choi S. Semi-supervised nonnegative matrix factorization. IEEE Signal Processing Letters 2010, 17:4-7.
27. Cai D., He X., Han J., Huang T.S. Graph regularized non-negative matrix factorization for data representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011, 33(8):1548-1560.
28. Cai D., He X., Han J. Locally consistent concept factorization for document clustering. IEEE Transactions on Knowledge and Data Engineering 2011, 23(6):902-913.
29. H. Lee, A. Battle, R. Raina, A.Y. Ng, Efficient sparse coding algorithms, in: Advances in Neural Information Processing Systems, vol. 19, pp. 801-808.
30. Zheng M., Bu J., Chen C.A., Wang C., Zhang L., Qiu G., Cai D. Graph regularized sparse coding for image representation. IEEE Transactions on Image Processing 2011, 20:1327-1336.
31. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning, Springer Series in Statistics 2009, Springer, New York.
32. Sha F., Lin Y., Saul L.K., Lee D.D. Multiplicative updates for nonnegative quadratic programming. Neural Computation 2007, 19:2004-2031.
33. Cai D., He X., Han J. Document clustering using locality preserving indexing. IEEE Transactions on Knowledge and Data Engineering 2005, 17:1624-1637.