A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples

Pattern Recognition

Volumn 44, Issue 6, 2011, Pages 1162-1171

  Zhang, Jianchun ^a   Zhang, Daoqiang ^a  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

Author keywords

Canonical correlation analysis; Ensemble construction; Feature extraction; Random correlation

Indexed keywords

CANONICAL CORRELATION ANALYSIS; DATA SETS; ENSEMBLE CONSTRUCTION; ENSEMBLE LEARNING; FEATURE EXTRACTION METHODS; FEATURE EXTRACTOR; MULTI-VIEWS; MULTIPLE VIEWS; NOVEL METHODS; RANDOM CORRELATION; RANDOM PROPERTIES; UNSUPERVISED METHOD;

CORRELATION METHODS; INFORMATION USE;

FEATURE EXTRACTION;

EID: 79551510424     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2010.12.011     Document Type: Article

References (25)

1. A. Blum, T. Mitchell, Combining labeled and unlabeled data with co-training, in: Proceedings of the Workshop on Computational Learning Theory, 1998.
2. K. Nigam, R. Ghani, Analyzing the effectiveness and applicability of co-training, in: Proceedings of Information and Knowledge Management, 2000, pp. 8693.
3. D. Yarowsky, Unsupervised word sense disambiguation rivaling supervised methods, in: Proceedings of the Association for Computational Linguistics, 1995, pp. 189196.
4. H. Hotelling Relation between two sets of variables Biometrica 28 1936 322 377
5. Z. Zhou, D. Zhan, Q. Yang, Semi-supervised learning with very few labeled training examples, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2007, pp. 675680.
6. S. Bickel, T. Scheffer, Multi-view clustering, in: Proceedings of the International Conference on Data Mining, 2004, pp. 1926.
7. K. Chaudhuri, S.M. Kakade, K. Livescu, K. Sridharan, Multi-view clustering via canonical correlation analysis, in: Proceedings of the International Conference on Machine Learning, 2009.
8. S. Kakade, and D. Foster Multi-view regression via canonical correlation analysis Learning Theory 2007 82 96
9. T.G. Dietterich, Ensemble methods in machine learning, in: Proceedings of the First International Workshop on Multiple Classifier Systems, 2000, pp. 115.
10. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140
11. Y. Freund, R.E. Schapire, Experiments with a new boosting algorithm, in: Proceedings of the Thirteenth International Conference Machine Learning, 1996, pp. 148156.
12. T.K. Ho The random subspace method for constructing decision forests Pattern Analysis and Machine Intelligence 20 8 1998 832 844
13. J.J. Rodrguez, L.I. Kuncheva, and C.J. Alonso Rotation forest: a new classifier ensemble method IEEE Transactions on Pattern Analysis and Machine Intelligence 28 10 2006 1619 1630
14. O. Okun, H. Priisalu, Multiple views in ensembles of nearest neighbor classifiers, in: Proceedings of the Workshop on Learning with Multiple Views, International Conference Machine Learning, 2005.
15. H. Guo, H.L. Viktor, Mining relational databases with multi-view learning, in: Proceedings of the International Workshop on Multi-relational Mining, 2005, pp. 1524.
16. Z. Zhou, and Y. Yu Adapt bagging to nearest neighbor classifiers Journal of Computer Science and Technology 20 2004 48 54
17. T. Sun, S. Chen, J. Yang, P. Shi, A novel method of combined feature extraction for recognition, in: Proceedings of the International Conference on Data Mining, 2008, pp. 10431048.
18. A. Shotaro, A kernel method for canonical correlation analysis, in: Proceedings of the International Meeting on Psychometric Society, 2001.
19. T. Diethe, D.R. Hardoon, J. Shawe-Taylor, Multiview fisher discriminant analysis, Technical Report, The NIPS Workshop Learning from Multiple Sources, 2008.
20. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification second ed. 2000 Wiley-Interscience
21. Q. Sun, S. Zeng, Y. Liu, P.A. Heng, and D. Xia A new method of feature fusion and its application in image recognition Pattern Recognition 38 12 2005 2437 2448 (Pubitemid 41336713)
22. I. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques second ed. 2005 Morgan Kaufmann
23. X. He, D. Cai, P. Niyogi, Laplacian score for feature selection, in: Advances in Neural Information Processing Systems, 2005, pp. 507514.
24. D. Cai, X. He, Y.H. Hu, J. Han, T. Huang, Learning a spatially smooth subspace for face recognition, in: Computer Vision and Pattern Recognition, 2007, pp. 17
25. T. Ahonen, A. Hadid, M. Pietikainen, Face recognition with local binary patterns, in: European Conference on Computer Vision, vol. 1, 2004, pp. 469481