Semi-supervised multi-label learning by constrained non-negative matrix factorization

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2006, Pages 421-426

  Liu, Yi ^a   Jin, Rong ^a   Yang, Liu ^a  

^a Michigan State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; DATA STRUCTURES; OPTIMIZATION; PATTERN RECOGNITION; PROBLEM SOLVING; SET THEORY;

MULTI LABEL LEARNING; NON-NEGATIVE MATRIX FACTORIZATION (NMF); OPTIMIZATION PROBLEMS; TEXT CATEGORIZATION;

LEARNING SYSTEMS;

EID: 33750718803     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Boutella, M. R.; Luob, J.; Shen, X.; and Browna, C. M. 2004. Learning multi-label scene classification. Pattern Recognition 37:1757-1771.
2. Crammer, K., and Singer, Y. 2002. A new family of online algorithms for category ranking. In SIGIR '02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in informaion retrieval.
3. Elisseeff, A., and JasonWeston. 2002. A kernel method for multi-labelled classification. In Dietterich, T. G.; Becker, S.; and Ghahramani, Z., eds., Advances in Neural Information Processing Systems 14, 681-687. Cambridge, MA: MIT Press.
4. Gao, S.; Wu, W.; Lee, C.-H.; and Chua, T.-S. 2004. A MFoM learning approach to robust multiclass multi-label text categorization. In ICML '04: Proceedings of the twenty-first international conference on Machine learning.
5. Ghamrawi, N., and McCallum, A. 2005. Collective multilabel classification. In CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management, 195-200. New York, NY, USA: ACM Press.
6. ImageCLEF. 2003. The CLEF Cross Language Image Retrieval Track (ImageCLEF), http://ir.shef.ac.uk/imageclef/.
7. Jin, R., and Ghahramani, Z. 2003. Learning with multiple labels. In S. Becker, S. T., and Obermayer, K., eds., Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press. 897-904.
8. Joachims, T. 1998. Text categorization with suport vector machines: Learning with many relevant features. In Proc European Conference on Machine Learning.
9. Joachims, T. 2003. Transductive learning via spectral graph partitioning. In Proceedings of the 20th International Conference on Machine Learning (ICML 2003).
10. Kazawa, H.; Izumitani, T.; Taira, H.; and Maeda, E. 2005. Maximal margin labeling for multi-topic text categorization. In Saul, L. K.; Weiss, Y.; and Bottou, L., eds., Advances in Neural Information Processing Systems 17. Cambridge, MA: MIT Press. 649-656.
11. McCallum, A. 1999. Multi-label text classification with a mixture model trained by EM. In AAAI'99 Workshop on Text Learning.
12. Rousu, J.; Saunders, C.; Szedmak, S.; and Shawe-Taylor, J. 2004. On maximum margin hierarchical multi-label classification. In NIPS Workshop on Learning With Structured Outputs.
13. Schapire, R. E., and Singer, Y. 2000. Boostexter: A boosting-based systemfor text categorization. Machine Learning 39(2-3).
14. Seeger, M. 2001. Learning with labeled and unlabeled data. Technical report, University of Edinburgh.
15. Taskar, B.; Chatalbashev, V.; and Koller, D. 2004. Learning associative markov networks. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, 102. New York, NY, USA: ACM Press.
16. Tsochantaridis, I.; Hofmann, T.; Joachims, T.; and Altun, Y. 2004. Support vector machine learning for interdependent and structured output spaces. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, 104. New York, NY, USA: ACM Press.
17. Ueda, N., and Saito, K. 2003. Parametric mixture models for multi-labeled text. In Saul, L. K.; Weiss, Y.; and Bottou, L., eds., Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press. 649-656.
18. Williams, C. 1998. Computation with infinite neural networks. Neural Computation 10(5).
19. Yang, Y. 1999. An evaluation of statistical approaches to text categorization. Information Retrieval 1(1/2).
20. Yu, K.; Yu, S.; and Tresp, V. 2005. Multi-label informed latent semantic indexing. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in informaion retrieval.
21. Zhou, D.; Schölkopf, B.; and Hofmann, T. 2005. Semisupervised learning on directed graphs.
22. Zhu, X., and Ghahramani, Z. 2003. Semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning (ICML 2003).
23. Zhu, S.; Ji, X.; Xu, W.; and Gong, Y. 2005. Multi-labelled classification using maximum entropy method. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 274-281. New York, NY, USA: ACM Press.
24. Zhu, X. 2006. Semi-supervised learning literature survey. Technical Report TR 1530, Computer Sciences, University of Wisconsin - Madison.