Communications inspired linear discriminant analysis

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 919-926

  Chen, Minhua ^a   Carson, William ^b   Rodrigues, Miguel ^c   Calderbank, Robert ^a   Carin, Lawrence ^a  

^a DUKE UNIVERSITY   (United States)

^b PA CONSULTING GROUP   (United Kingdom)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALTERNATIVE APPROACH; CLASS LABELS; EMPIRICAL COMPARISON; GRADIENT DESCENT; GRADIENT OF MUTUAL INFORMATION; LINEAR DISCRIMINANT ANALYSIS; LINEAR PROJECTIONS; MUTUAL INFORMATIONS; OBJECTIVE FUNCTIONS; OPTIMIZATION PROBLEMS; PARAMETER SETTING; REAL DATA SETS; SUPERVISED LINEAR DIMENSIONALITY REDUCTION; THEORETICAL RESULT;

INFORMATION THEORY; LEARNING SYSTEMS;

FACE RECOGNITION;

EID: 84867120235     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Ashok, A., Baheti, P., and Neifeld, M. Compressive imaging system design using task-specific information. Applied Optics, 47(25):4457-4471, 2008.
2. Baheti, P. and Neifeld, M. Recognition using information-optimal adaptive feature-specific imaging. Journal of the Optical Society of America A, 26(4): 1055-1070, 2009.
3. Blei, D. and Jordan, M. Variational inference for dirichlet process mixtures. Bayesian Analysis, 1(1):121-144, 2006.
4. Candes, E. and Wakin, M. An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2) :21-30, 2008.
5. Carson, W., Rodrigues, M., Chen, M., Carin, L., and Calderbank, R. How to focus the discriminative power of a dictionary. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 2012.
6. Chen, M., Silva, J., Paisley, J., Wang, C., Dunson, D., and Carin, L. Compressive sensing on manifolds using a non-parametric mixture of factor analyzers: Algorithm and performance bounds. IEEE Trans. Signal Processing, 58 (12):6140-6155, 2010.
7. Cover, T. and Thomas, J. Elements of Information Theory. Wiley, New York, 2006.
8. Duarte-Carvajalino, J. and Sapiro, G. Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization. IEEE Trans. Image Processing, 18(7):1395-1408, 2009.
9. Fano, R. Transmission of Information: A Statistical theory of Communications. Wiley, New York, 1961.
10. Fisher, R. The use of multiple measurements in taxonomic problems. Annals of Human Genetics, 7(2):179-188, 1936.
11. Hamsici, O. and Martinez, A. Bayes optimality in linear discriminant analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, 30(4):647-657, 2008.
12. Hellman, M. and Raviv, J. Probability of error, equivocation, and the chernoff bound. IEEE Trans. Information Theory, 16(4):368-372, 1970.
13. Hild, K., Erdogmus, D., Torkkola, K., and Principe, J. Feature extraction using information-theoretic learning. IEEE Trans. Pattern Analysis and Machine Intelligence, 28(9):1385-1392, 2006.
14. Kaski, S. and Peltonen, J. Informative discriminant analysis. In Proceedings of the Twentieth International Conference on Machine Learning (ICML), Washington DC, USA, 2003.
15. Liu, L. and Fieguth, P. Texture classification from random features. IEEE Trans. Pattern Analysis and Machine Intelligence, 34(3):574-586, 2012.
16. Nenadic, Z. Information discriminant analysis: Feature extraction with an information-theoretic objective. IEEE Trans. Pattern Analysis and Machine Intelligence, 29 (8): 1394-1407, 2007.
17. Palomar, D. and Verdu, S. Gradient of mutual information in linear vector gaussian channels. IEEE Trans. Information Theory, 52(1):141-154, 2006.
18. Song, L., Smola, A., Borgwardt, K., and Gretton, A. Colored maximum variance unfolding. In In Advances in Neural Information Processing Systems 20 (NIPS), Vancouver, BC, Canada, 2007. MIT Press.
19. Tao, D., Li, X., Wu, X., and Maybank, S. Geometric mean for subspace selection. IEEE Trans. Pattern Analysis and Machine Intelligence, 31(2):260-274, 2009.
20. Tenenbaum, J., Silva, V., and Langford, J. A global geometric framework for nonlinear dimensionality reduction. Science, 290(22):2319-2323, 2000.
21. Torkkola, K. Learning discriminative feature transforms to low dimensions in low dimensions. In In Advances in Neural Information Processing Systems 14 (NIPS), Vancouver, BC, Canada, 2001. MIT Press.
22. Torkkola, K. Feature extraction by non-parametric mutual information maximization. Journal of Machine Learning Research, 3:1415-1438, 2003.
23. Xiao, C., Zheng, Y., and Ding, Z. Globally optimal linear precoders for finite alphabet signals over complex vector gaussian channels. IEEE Trans. Signal Processing, 59 (7):3301-3314, 2011.