Exponential family sparse coding with applications to self-taught learning

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2009, Pages 1113-1119

  Lee, Honglak ^a   Raina, Rajat ^a   Teichman, Alex ^a   Ng, Andrew Y ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LEARNING ALGORITHMS; MACHINE LEARNING; POISSON DISTRIBUTION; SUPERVISED LEARNING; TEXT PROCESSING;

ALGORITHM FOR SOLVING; EXPONENTIAL FAMILY DISTRIBUTIONS; GAUSSIAN NOISE MODEL; GENERALIZED LINEAR MODEL; QUADRATIC LOSS FUNCTIONS; REGULARIZED OPTIMIZATION PROBLEMS; SELF-TAUGHT LEARNING; TEXT CLASSIFICATION;

CODES (SYMBOLS);

EID: 78751681286     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. G. Andrew and J. Gao. Scalable training of L1-regularized log-linear models. In ICML, 2007.
2. D. Blei, A. Y. Ng, and M.I. Jordan. Latent dirichlet allocation. JMLR, 3:993-1022, 2003.
3. S. Boyd and L. Vandenberghe. Convex Optimization. Camb. Univ. Press, 2004.
4. Michael Collins, Sanjoy Dasgupta, and Robert E. Schapire. A generalization of principal component analysis to the exponential family. In NIPS, 2001.
5. C. Do and A. Y. Ng. Transfer learning for text classification. In NIPS, 2006.
6. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. Least angle regression. Ann. Stat., 32, 2004.
7. J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani. Pathwise coordinate optimization. Annals of Applied Statistics, 2, 2007.
8. J. Goodman. Exponential priors for maximum entropy models. In ACL, 2004.
9. P. J. Green. Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives. JRSS, Ser. B, Meth., 46:149-192, 1984.
10. Andrew E. Johnson and Martial Hebert. Using spin images for efficient object recognition in cluttered 3d scenes. IEEE PAMI, 21(5):433-449, 1999.
11. 1-regularized logistic regression. JMLR, 8:1519-1555, 2007.
12. 1 regularized logistic regression. In AAAI, 2006.
13. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In NIPS, 2007.
14. J. Lokhorst. The Lasso and Generalised Linear Models. Hons. Project, Department of Statistics, The University of Adelaide, Australia, 1999.
15. P. McCullagh and J. Nelder. Generalized Linear Models. Chapman & Hall, 1989.
16. 2 regularization, and rotational invariance. In ICML, 2004.
17. B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583), 1996.
18. S. Perkins and J. Theiler. Online feature selection using grafting. In ICML, 2003.
19. R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught learning: Transfer learning from unlabeled data. In ICML, 2007.
20. V. Roth. The generalized lasso. IEEE Trans. Neural Networks, 15(1):16-28, 2004.
21. J. Weston, R. Collobert, F. Sinz, L. Bottou, and V. Vapnik. Inference with the universum. In ICML, 2006.
22. J. Yu, S. V. N. Vishwanathan, S. Günter, and N. N. Schraudolph. A quasi-Newton approach to nonsmooth convex optimization. In ICML, 2008.