Multilabel classification using Bayesian compressed sensing

Advances in Neural Information Processing Systems

Volumn 4, Issue , 2012, Pages 2645-2653

  Kapoor, Ashish ^a   Jain, Prateek ^a   Viswanathan, Raajay ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN COMPRESSED SENSING; BAYESIAN FRAMEWORKS; MULTI-LABEL CLASSIFICATIONS; POSTERIOR DISTRIBUTIONS; PREDICTION PERFORMANCE; PROBABILISTIC MODELS; UNCERTAINTY ESTIMATES; VARIATIONAL INFERENCE;

BENCHMARKING; FORECASTING; UNCERTAINTY ANALYSIS; VECTOR SPACES;

SIGNAL RECONSTRUCTION;

EID: 84877734258     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. D. Hsu, S. Kakade, J. Langford, and T. Zhang. Multi-label prediction via compressed sensing. In NIPS, pages 772-780, 2009.
2. B. Hariharan, L. Zelnik-Manor, S. V. N. Vishwanathan, and M. Varma. Large scale max-margin multilabel classification with priors. In ICML, pages 423-430, 2010.
3. G. Tsoumakas and I. Katakis. Multi-label classification: An overview. IJDWM, 3(3):1-13, 2007.
4. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6:1453-1484, 2005.
5. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771, 2004.
6. B. Taskar, C. Guestrin, and D. Koller. Max-margin markov networks. In NIPS, 2003.
7. R. M. Rifkin and A. Klautau. In defense of one-vs-all classification. Journal of Machine Learning Research, 5:101-141, 2004.
8. D. Needell and J. A. Tropp. Cosamp: Iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis, 26(3):301-321, 2009.
9. S. Foucart. Hard thresholding pursuit: an algorithm for compressive sensing, 2010. preprint.
10. D. Baron, S. S. Sarvotham, and R. G. Baraniuk. Bayesian compressive sensing via belief propagation. IEEE Transactions on Signal Processing, 58(1), 2010.
11. S. Ji, Y. Xue, and L. Carin. Bayesian compressive sensing. IEEE Transactions on Signal Processing, 56(6), 2008.
12. N. Cesa-Bianchi, A Conconi, and C. Gentile. Learning probabilistic linear-threshold classifiers via selective sampling. In COLT, 2003.
13. N. Lawrence, M. Seeger, and R. Herbrich. Fast sparse Gaussian Process method: Informative vector machines. NIPS, 2002.
14. D. MacKay. Information-based objective functions for active data selection. Neural Computation, 4(4), 1992.
15. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In ICML, 2000.
16. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2-3), 1997.
17. B. Yang, J.-Tao Sun, T.Wang, and Z. Chen. Effective multi-label active learning for text classification. In KDD, pages 917-926, 2009.
18. J. Weston, S. Bengio, and N. Usunier. Large scale image annotation: learning to rank with joint word-image embeddings. Machine Learning, 81(1):21-35, 2010.
19. C. E. Rasmussen and C. K. I.Williams. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, 2005.
20. M. E. Tipping. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211-244, 2001.