Supervised Transfer Sparse Coding


  • Maruan Al-Shedivat King Abdullah University of Science and Technology
  • Jim Jing-Yan Wang University at Buffalo, The State University of New York
  • Majed Alzahrani King Abdullah University of Science and Technology
  • Jianhua Huang Texas A&M University
  • Xin Gao King Abdullah University of Science and Technology



Sparse coding, Transfer learning, Supervised learning, Classification, Support Vector Machine


A combination of the sparse coding and transfer learning techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from different underlying distributions, i.e., belong to different domains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small number of them. In this paper, we explore such possibility and show how a small number of labeled data in the target domain can significantly leverage classification accuracy of the state-of-the-art transfer sparse coding methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.




How to Cite

Al-Shedivat, M., Wang, J. J.-Y., Alzahrani, M., Huang, J., & Gao, X. (2014). Supervised Transfer Sparse Coding. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Novel Machine Learning Algorithms