Feature selection by joint graph sparse coding

Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

Volumn , Issue , 2013, Pages 803-811

  Zhu, Xiaofeng ^a   Wu, Xindong ^b   Ding, Wei ^c   Zhang, Shichao ^d  

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b UNIVERSITY OF VERMONT   (United States)

^c UNIVERSITY OF MASSACHUSETTS BOSTON   (United States)

^d GUANGXI NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; GRAPH THEORY; CODES (SYMBOLS);

EMPIRICAL STUDIES; GLOBAL OPTIMUM; K-NN CLASSIFICATIONS; MANIFOLD LEARNING; OBJECTIVE FUNCTIONS; REAL DATA SETS; SPARSE CODING; STATE-OF-THE-ART ALGORITHMS;

DATA MINING;

EID: 84903907116     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972832.89     Document Type: Conference Paper

References (18)

1. Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
2. Deng Cai, Chiyuan Zhang, and Xiaofei He. Unsupervised feature selection for multi-cluster data. In ACM Special Interest Group on Knowledge Discovery and Data Mining, pages 333-342, 2010.
3. Shenghua Gao, Ivor Wai-Hung Tsang, Liang-Tien Chia, and Peilin Zhao. Local features are not lonely-laplacian sparse coding for image classification. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3555-3561, 2010.
4. Xiaofei He, Deng Cai, and Partha Niyogi. Laplacian score for feature selection. In Neural Information Processing Systems, pages 1-8, 2005.
5. Xiaofei He and Partha Niyogi. Locality preserving projections. In Neural Information Processing Systems, pages 197-204, 2003.
6. Sotiris Kotsiantis. Feature selection for machine learning classification problems: a recent overview. Artificial Intelligence Review, pages 1-20, 2011.
7. Honglak Lee, Rajat Raina, Alex Teichman, and Andrew Y. Ng. Exponential family sparse coding with applications to self-taught learning. In International Joint Conference on Artificial Intelligence, pages 1113-1119, 2009.
8. Julien Mairal, Francis Bach, Jean Ponce, and Guillermo Sapiro. Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11:19-60, 2010.
9. Sebastián Maldonado and Richard Weber. A wrapper method for feature selection using support vector machines. Information Science Journal, 179:2208-2217, 2009.
10. Feiping Nie, Heng Huang, Xiao Cai, and Chris Ding. Efficient and robust feature selection via joint l2,1-norms minimization. In Neural Information Processing Systems, pages 1813-1821, 2010.
11. Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y. Ng. Self-taught learning: transfer learning from unlabeled data. In International Conference on Machine Learning, pages 759-766, 2007.
12. Zenglin Xu, Rong Jin, Jieping Ye, and Michael R. Lyu. Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural Networks, 21(7):1033-1047, 2010.
13. Yi Yang, Heng Tao Shen, Zhigang Ma, Zi Huang, and Xiaofang Zhou. l2, 1-norm regularized discriminative feature selection for unsupervised learning. In International Joint Conferences on Artificial Intelligence, pages 1589-1594, 2011.
14. Ming Yuan and Yi Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B, 68:49-67, 2006.
15. Zheng Zhao, Lei Wang, and Huang Liu. Efficient spectral feature selection with minimum redundancy. In AAAI Conference on Artificial Intelligence, 2010.
16. Miao Zheng, Jiajun Bu, Chun Chen, Can Wang, Lijun Zhang, Guang Qiu, and Deng Cai. Graph regularized sparse coding for image representation. IEEE Transactions on Image Processing, 20(5):1327-1336, 2011.
17. Xiaofeng Zhu, Zi Huang, Heng Tao Shen, Jian Cheng, and Changsheng Xu. Dimensionality reduction by mixed kernel canonical correlation analysis. Pattern Recognition, 45(8):3003-3016, 2012.
18. Xiaofeng Zhu, Zi Huang, Yang Yang, Heng Tao Shen, Changsheng Xu, and Jiebo Luo. Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognition, 46(1):215-229, 2013.