Semi-supervised distance metric learning based on local linear regression for data clustering

Neurocomputing

Volumn 93, Issue , 2012, Pages 100-105

  Zhang, Hong ^a   Yu, Jun ^b   Wang, Meng ^c   Liu, Yun ^d  

^a WUHAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b XIAMEN UNIVERSITY   (China)

^c HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^d NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Data clustering; Distance metric; Semi supervised learning

Indexed keywords

DATA CLUSTERING; DATA DISTRIBUTION; DISCRIMINATIVE ABILITY; DISTANCE METRIC LEARNING; DISTANCE METRICS; FEATURE CORRELATION; FEATURE SPACE; LOCAL LINEAR REGRESSION; OBJECTIVE FUNCTIONS; PREDICTION ERRORS; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; UNLABELED SAMPLES; WITHIN-CLASS COVARIANCE;

SUPERVISED LEARNING;

CLUSTERING ALGORITHMS;

ALGORITHM; ANALYTICAL ERROR; ARTICLE; CALCULATION; CORRELATION ANALYSIS; DATA CLUSTERING; DATA PROCESSING; DISTANCE METRIC; LINEAR REGRESSION ANALYSIS; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PREDICTION; PRIORITY JOURNAL;

EID: 84862198325     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.03.007     Document Type: Article

References (36)

1. B. Alipanahi, M. Biggs, A. Ghodsi, Distance metric learning versus fisher discriminant analysis, in: In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI), 2008.
2. M. Baghshah, S. Shouraki, Semi-supervised metric learning using pairwise constraints, in: International Joint Conference on Artificial Intelligence (IJCAI), 2009, pp. 1217-1222.
3. Belhumeur P., Hespanha J., Kriegman D. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 1997, 19(7):711-720.
4. D. Cai, X. He, J. Han, Semi-supervised discriminant analysis, in: IEEE 11th International Conference on Computer Vision. IEEE, 2007, pp. 1-7.
5. F. Chung, Spectral graph theory, in: Regional Conference Series in Mathematics, vol. 92, AMS, 1997.
6. J. Davis, B. Kulis, P. Jain, S. Sra, I. Dhillon, Information-theoretic metric learning, in: In Proceedings of the International Conference on Machine Learning (ICML), 2007.
7. Georghiades A., Belhumeur P., Kriegman D. From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 2001, 23(6):643-660.
8. A. Globerson, S. Roweis, Metric learning by collapsing classes, in: Advances in NIPS, MIT Press, 2006, pp. 451-458.
9. J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov, Neighbourhood components analysis, in: Advances in Neural Information Processing Systems (NIPS), MIT Press, 2004.
10. X. He, W. Ma, H. Zhang, Learning an image manifold for retrieval, in: ACM Multimedia Conference, ACM, 2004.
11. D. Klein, S. Kamvar, C. Manning, From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering, 19th International Conference on Machine Learning, vol. 9, Sydney, 2002.
12. Lovasz L., Plummer M. Matching Theory 1986, Akadmiai Kiad, North Holland, Budapest.
13. Nie F., Xiang S., Jia Y., Zhang C. Semi-supervised orthogonal discriminant analysis via label propagation. Pattern Recogn. 2009, 42(11):2615-2627.
14. F. Nie, D. Xu, I. Tsang, C. Zhang, Spectral embedded clustering, in: International Joint Conference on Artificial Intelligence (IJCAI), California, 2009, pp. 1181-1186.
15. Nie F., Xu D., Wai-Huang T., Zhang C. Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. IEEE Trans. Image Process. 2010, 19(7):1921-1953.
16. Roweis S., Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
17. Sim T., Baker S., Bsat M. Illumination and expression on database. IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25(12):1615-1618.
18. Tang J., Hua X., Qi G., Song Y., Wu X. Video annotation based on kernel linear neighborhood propagation. IEEE Trans. Multimedia 2008, 10(4):620-628.
19. Tang J., Li H., Qi G., Chua T. Image annotation by graph-based inference with integrated multiple/single instance representations. IEEE Trans. Multimedia 2010, 12(2):131-141.
20. Tenenbaum J., Silva V., Langford J. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2323.
21. L. Torresani, K. Lee, Large margin component analysis, in: Advances in NIPS, MIT Press, 2007, pp. 505-512.
22. Wang M., Hua X., Tang J., Hong R. Beyond distance measurement: constructing neighborhood similarity for video annotation. IEEE Trans. Multimedia 2009, 11(3):465-476.
23. K. Weinberger, J. Blitzer, L. Saul, Distance metric learning for large margin nearest neighbor classification, in: In Advances in Neural Information Processing Systems (NIPS), MIT Press, 2006.
24. Wu F., Wang W., Yang Y. Classification by semi-supervised discriminative regularization. Neurocomputing 2010, 73:1641-1651.
25. E. Xing, A. Ng, M. Jordan, S., R., Distance metric learning with application to clustering with side information, in: Advances in NIPS, MIT Press, 2003, pp. 505-512.
26. Y. Yang, R. Ji, F. Nie, H. Shen, X. Zhou, Nonnegative spectral clustering with discriminative regularization, in: AAAI Conference on Artificial Intelligence, 2011.
27. Yang Y., Nie F., Xu D., Luo J., Zhuang Y., Pan Y. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34(4):723-742.
28. Y. Yang, D. Xu, F. Nie, J. Luo, Y. Zhuang, Ranking with local regression and global alignment for cross-media retrieval, in: ACM Multimedia Conference, ACM, 2009.
29. Yang Y., Xu D., Nie F., Yan S., Zhuang Y. Image clustering using local discriminant models and global integration. IEEE Trans. Image Process. 2010, 19(10):2761-2773.
30. Yang Y., Zhuang Y., Wu F., Pan Y. Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval. IEEE Trans. Multimedia 2008, 10(3):437-446.
31. Y. Yang, Y. Zhuang, D. Xu, Y. Pan, Retrieval based interactive cartoon synthesis via unsupervised bi-distance metric learning, in: ACM Multimedia Conference, ACM, 2009, pp. 311-320.
32. Yu J., Liu D., Tao D., Seah H. Complex object correspondence construction in two-dimensional animation. IEEE Trans. Image Process. 2011, 20(11):3257-3269.
33. J. Yu, D. Tao, M. Wang, Adaptive hypergraph learning and its application in image classification, IEEE Trans. Image Process., doi:10.1109/TIP.2012.2190083.
34. Zhang H., Wang Y., Pan H., Wu F. Understanding visual-auditory correlation from heterogeneous features for cross-media retrieval. J. Zhejiang Univ. Sci. A 2008, 9:241-249.
35. H. Zhang, Y. Zhuang, F. Wu, Cross-modal correlation learning for clustering on image-audio dataset, in: The 15th ACM International Conference on Multimedia, ACM, 2007.
36. Zhuang Y., Yu J., Xiao J., Chen C. Perspective-aware cartoon clips synthesis. Comput. Animat. Vir. World 2008, 19(3):355-364.