Heteroscedastic probabilistic linear discriminant analysis with semi-supervised extension

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5782 LNAI, Issue PART 2, 2009, Pages 602-616

  Zhang, Yu ^a   Yeung, Dit Yan ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC RELEVANCE DETERMINATION; DIMENSIONALITY REDUCTION; HETEROSCEDASTIC; HOMOSCEDASTICITY; LINEAR DISCRIMINANT ANALYSIS; LOW-DIMENSIONAL SPACES; MISSING DATA; OBJECT DATABASE; PROBABILISTIC OUTPUT; SEMI-SUPERVISED; SMALL SAMPLE SIZE PROBLEMS; SUB-SPACE METHODS;

DATABASE SYSTEMS; DISCRIMINANT ANALYSIS; FACE RECOGNITION; IMAGE RETRIEVAL; ROBOT LEARNING;

PROBABILITY DISTRIBUTIONS;

EID: 70349952329     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04174-7_39     Document Type: Conference Paper

References (36)

1. Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2002)
2. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1991)
3. Chen, L., Liao, H., Ko, M., Lin, J., Yu, G.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33(10), 1713-1726 (2000)
4. Krzanowski, W.J., Jonathan, P., McCarthy, W.V., Thomas, M.R.: Discriminant analysis with singular covariance matrices: methods and applications to spectroscopic data. Applied Statistics 44(1), 101-115 (1995)
5. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711-720 (1997)
6. Ye, J., Li, Q.: A two-stage linear discirminant analysis via QR-decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 929-941 (2005)
7. Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(1), 4-13 (2005)
8. Wang, X., Tang, X.: Dual-space linear discriminant analysis for face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, pp. 564-569 (2004)
9. Ye, J., Janardan, R., Li, Q.: Two-dimensional linear discriminant analysis. In: Advances in Neural Information Processing Systems 17, Vancouver, British Columbia, Canada, pp. 1529-1536 (2005)
10. Cai, D., He, X., Han, J.: Semi-supervised discriminant analysis. In: Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil (2007)
11. Zhang, Y., Yeung, D.Y.: Semi-supervised discriminant analysis using robust path-based similarity. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska (2008)
12. Zhang, Y., Yeung, D.Y.: Semi-supervised discriminant analysis via CCCP. In: Proceedings of the 19th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Antwerp, Belgium, pp. 644-659 (2008)
13. Chapelle, O., Zien, A., Schölkopf, B. (eds.): Semi-Supervised Learning. MIT Press, Boston (2006)
14. Hastie, T., Tibshirani, R.: Discriminant analysis by Gaussian mixture. Journal of the Royal Statistical Society, Series B 58(1), 155-176 (1996)
15. Kumar, N., Andreou, A.G.: Heteroscedastic discriminant analysis and reduced rank HMMS for improved speech recognition. Speech Communication 26(4), 283-297 (1998)
16. Loog, M., Duin, R.P.W.: Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(6), 732-739 (2004)
17. Dai, G., Yeung, D.Y., Chang, H.: Extending kernel fisher discriminant analysis with the weighted pairwise Chernoff criterion. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol.3954, pp. 308-320. Springer, Heidelberg (2006)
18. Ioffe, S.: Probabilistic linear discriminant analysis. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol.3954, pp. 531-542. Springer, Heidelberg (2006)
19. Yu, S., Yu, K., Tresp, V., Kriegel, H.P.,Wu,M.: Supervised probabilistic principal component analysis. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, pp. 464-473 (2006)
20. Prince, S.J.D., Elder, J.H.: Probabilistic linear discriminant analysis for inferences about identity. In: Proceedings of the 11th IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1-8 (2007)
21. Neal, R.M.: Bayesian Learning for Neural Network. Springer, New York (1996)
22. Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistic Society, B 61(3), 611-622 (1999)
23. Bach, F.R., Jordan, M.I.: A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley (2005)
24. He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 328-340 (2005)
25. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research 7, 2399-2434 (2006)
26. Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems 17, Vancouver, British Columbia, Canada, pp. 1601-1608 (2005)
27. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistic Society, B 39(1), 1-38 (1977)
28. Tipping, M.E., Bishop, C.M.: Mixtures of probabilistic principal component analysers. Neural Computation 11(2), 443-482 (1999)
29. Joachims, T.: Transductive inference for text classification using support vector machines. In: Proceedings of the Sixteenth International Conference on Machine Learning, San Francisco, CA, USA, pp. 200-209 (1999)
30. Bennett, K., Demiriz, A.: Semi-supervised support vector machines. In: Advances in Neural Information Processing Systems 11, Vancouver, British Columbia, Canada, pp. 368-374 (1998)
31. MacKay, D.J.C.: Comparison of approximate methods for handling hyperparameters. Neural Computation 11(5), 1035-1068 (1999)
32. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
33. Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71-86 (1991)
34. Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1615-1618 (2003)
35. Nene, S.A., Nayar, S.K., Murase, H.: Columbia object image library (COIL-20). Technical Report 005, CUCS (1996)
36. Lawrence, N.D.: Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research 6, 1783 - 1816 (2005)