Project: Large-Scale Multi-label Learning (NSF IIS-0643494)

 

PI: Rong Jin (rongjin@cse.msu.edu), Michigan State University

 

Abstract:

Important applications in science and business depend on automatic classification. Multi-label learning refers to the classification problem where each example can be assigned to multiple class labels simultaneously. It has found applications in many different domains, such as natural language processing, computer vision, human computer interaction, bioinformatics, health care, and physiology. Existing machine learning technologies are unsuitable for large-scale multi-label learning because they are unable to handle rare class classification problems and distinguish classes with similar input patterns.

 

To overcome the limitation of the existing approaches, the project will develop a relation propagation framework  for multi-label learning that explicitly exploits the similarity of examples and the correlation among classes simultaneously. In particular, this project will include research to (1) develop efficient optimization algorithms for the proposed relation propagation framework; (2) develop effective algorithms for learning the similarity of examples and the correlation among classes; (3) develop effective active learning algorithms for multi-label learning; and (4) evaluate the proposed framework for multi-label learning through three real world applications.

 

The project will advance the state of the art of techniques for large-scale multi-label learning through the development of relation propagation framework, which in return will have a significant impact on a wide range of applications. The research results will also enhance the current machine learning curricula, and improve the education of the information technology workforce.

 

Publication:

 

  1. S. C. H. Hoi, R. Jin, J. Zhu, and M. Lyu, Semi-Supervised SVM Batch Mode Active Learning for Image Retrieval, to appear in the Proceedings of IEEE Computer Society on Computer Vision and Pattern Recognition (CVPR 2008), 2008 (PDF)
  2. R. Jin, H. Valizadegan, and L. Hang, Ranking Refinement and Its Application to Information Retrieval, Proceedings of 17th International World Wide Web Conference (WWW 2008), 397-406, 2008 (PDF)
  3. S. C. H. Hoi and R. Jin, Active Kernel Learning, Proceedings of the 25th International Conference on Machine Learning (ICML 2008), 400-407, 2008 (PDF)
  4. Yang Zhou, Zheng Li, Xuerui Yang, Linxia Zhang, Shireesh Srivastava, Rong Jin, Christina Chan: Using Knowledge Driven Matrix Factorization to Reconstruct Modular Gene Regulatory Network. Proceedings of 23rd National Conference on Artificial Intelligence (AAAI 2008), 811-816, 2008 (PDF)
  5. S. C. H. Hoi and R. Jin, Semi-Supervised Ensemble Ranking, Proceedings of 23rd National Conference on Artificial Intelligence (AAAI 2008), 643-649, 2008 (PDF)
  6. H. Valizadegan, R. Jin, and A. K. Jain, Semi-supervised Boosting for Multi-Class Classification, Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD 2008), 522-537, 2008 (PDF)
  7. J. Chai, C. Zhang, and R. Jin, An Empirical Investigation of User Term Feedback in Targeted Image Search via Text-based Retrieval, ACM Transactions on Information Systems, 25(1), February, 2007. (PDF)
  8. Y. Liu, R. Jin, and A. Jain, BoostCluster: Boosting Clustering by Pairwise Constraints, Proceedings of Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), 450-459, 2007 (PDF)
  9. L. Yang, R. Jin, and R. Sukthankar, Bayesian Active Distance Metric Learning, Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI 2007), 2007 (PDF)
  10. L. Yang, R. Jin, and R. Sukthankar, Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data, Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 1-8, 2007 (PDF)

 

Software:

 

  1. We have developed a package for distance metric learning that implements a number of state-of-the-art algorithms. More information of this package can be found from the web page http://www.cse.msu.edu/~yangliu1/distlearn.htm.

 

Guest lectures:

 

1.      An introductory lecture of Machine Learning lecture for the course of introductory bioinformatics (Plant Biology) (ppt file)

 

Students:

 

  1. Tianbao Yang (joined the project since Aug., 2008)
  2. Liu Yang (http://www.cse.msu.edu/~yangliu1/) (graduated on July, 2008)
  3. Feng Kang (graduated on Dec., 2007)