Multi-view clustering and feature learning via structured sparsity

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 2, 2013, Pages 1389-1397

  Wang, Hua ^a   Nie, Feiping ^b   Huang, Heng ^b  

^a Department of Metallurgical and Materials Engineering   (United States)

^b UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA FUSION;

CLASSIFICATION TASKS; DATA FUSION METHODS; FEATURE LEARNING; MULTI-VIEW CLUSTERING; MULTI-VIEW LEARNING; SCIENTIFIC APPLICATIONS; STATE-OF-THE-ART METHODS; STRUCTURED SPARSITIES;

LEARNING SYSTEMS;

EID: 84897568262     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. Argyriou, Andreas, Evgeniou, Theodoros, and Pontil, Massimiliano. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.
2. Bach, F., Lanckriet, G.R.G., and Jordan, M.I. Multiple Kernel Learning, Conic Duality, and the SMO Algorithm. ICML, 2004.
3. Belkin, M, Niyogi, P, and Sindhwani, V. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. JMLR, 1:1-48, 2006.
4. Bickel, Steffen and Scheffer, Tobias. Multi-view clustering. IEEE International Conference on Data Mining, 2004.
5. Brefeld, Ulf and Scheffer, Tobias. Co-em support vector learning. International Conference on Machine Learning, 2004.
6. Cai, X., Nie, F., Huang, H., and Kamangar, F. Heterogeneous image feature integration via multi-modal spectral clustering. In CVPR, 2011.
7. Ghani, R. Combining labeled and unlabeled data for multiclass text categorization. International Conference on Machine Learning, 2002.
8. Gorodnitsky, I.F. and Rao, B.D. Sparse signal reconstruction from limited data using focuss: A re-weighted minimum norm algorithm. Signal Processing, IEEE Transactions on, 45(3):600-616, 1997. (Pubitemid 127765947)
9. He, X., Yan, S., Hu, Y., Niyogi, P., and Zhang, H.J. Face recognition using laplacianfaces. IEEE TPAMI, 27(3):328-340, 2005. (Pubitemid 40377326)
10. Kloft, M., Brefeld, U., Laskov, P., and Sonnenburg, S. Nonsparse multiple kernel learning. In NIPS.
11. Kumar, A., Rai, P., and Daumé III, H. Co-regularized multi-view spectral clustering. In NIPS, 2011.
12. Lanckriet, G.R.G., Cristianini, N., Bartlett, P., Ghaoui, L. E., and Jordan, M.I. Learning the kernel matrix with semidefinite programming. JMLR, 5:27-72, 2004a. ISSN 1532-4435.
13. Lanckriet, G.R.G., De Bie, T., Cristianini, N., Jordan, M. I., and Noble, W.S. A statistical framework for genomic data fusion. Bioinformatics, 2004b. ISSN 1367-4803.
14. Ng, Andrew Y., Jordan, Michael I., and Weiss, Yair. On spectral clustering: Analysis and an algorithm. In NIPS, pp. 849-856, 2001.
15. Nie, Feiping, Xu, Dong, Tsang, Ivor W., and Zhang, Changshui. Spectral embedded clustering. In IJCAI, pp. 1181-1186, 2009.
16. Obozinski, Guillaume, Taskar, Ben, and Jordan, Michael I. Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing, 20:231-252, 2010.
17. Prettenhofer, P. and Stein, B. Cross-language text classification using structural correspondence learning. In ACL, 2010.
18. 1,∞ Regularization. ICML, 2009.
19. Sonnenburg, S., Rätsch, G., Schäfer, C., and Schölkopf, B. Large scale multiple kernel learning. JMLR, 7:1531-1565, 2006. ISSN 1532-4435.
20. Suykens, J.A.K., Van Gestel, T., and De Brabanter, J. Least squares support vector machines. World Scientific Pub Co Inc, 2002. ISBN 9812381511.
21. Wang, Hua, Nie, Feiping, Huang, Heng, Risacher, Shannon, Saykin, Andrew J, and Shen, Li. Identifying adsensitive and cognition-relevant imaging biomarkers via joint classification and regression. In MICCAI 2011, pp. 115-123. Springer, 2011.
22. Wang, Hua, Nie, Feiping, Huang, Heng, Kim, Sungeun, Nho, Kwangsik, Risacher, Shannon L, Saykin, Andrew J, Shen, Li, et al. Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the adni cohort. Bioinformatics, 28(2):229-237, 2012a.
23. Wang, Hua, Nie, Feiping, Huang, Heng, Risacher, Shannon L, Saykin, Andrew J, Shen, Li, et al. Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics, 28(12):i127-i136, 2012b.
24. Wang, Hua, Nie, Feiping, Huang, Heng, Yan, Jingwen, Kim, Sungeun, Nho, Kwangsik, Risacher, Shannon L., Saykin, Andrew J., Shen, Li, and for the Alzheimer's Disease Neuroimaging Initiative. From phenotype to genotype: an association study of longitudinal phenotypic markers to alzheimer's disease relevant snps. Bioinformatics, 28(18):i619-i625, 2012c.
25. Wang, Hua, Nie, Feiping, Huang, Heng, Yan, Jingwen, Kim, Sungeun, Risacher, Shannon, Saykin, Andrew, and Shen, Li. High-order multi-task feature learning to identify longitudinal phenotypic markers for alzheimer's disease progression prediction. In NIPS, 2012d.
26. Wang, Hua, Nie, Feiping, Huang, Heng, and Ding, Chris. Heterogeneous Visual Features Fusion via Sparse Multimodal Machine. In CVPR 2013, 2013.
27. Ye, J., Ji, S., and Chen, J. Multi-class discriminant kernel learning via convex programming. JMLR, 9:719-758, 2008a. ISSN 1532-4435.
28. Ye, Jieping, Zhao, Zheng, and Wu, Mingrui. Discriminative k-means for clustering. In NIPS, pp. 1649-1656, 2008b.
29. Yu, S., Falck, T., Daemen, A., Tranchevent, L.C., Suykens, J.A.K., De Moor, B., and Moreau, Y. L 2-norm multiple kernel learning and its application to biomedical data fusion. BMC bioinformatics, 11(1):309, 2010. ISSN 1471-2105.
30. Yuan, M. and Lin, Y. Model selection and estimation in regression with grouped variables. Journal of The Royal Statistical Society Series B, 68(1):49-67, 2006. (Pubitemid 43415335)