Dictionary Learning with Mutually Reinforcing Group-Graph Structures


  • Hongteng Xu Georgia Institute of Technology
  • Licheng Yu University of North Carolina at Chapel Hill
  • Dixin Luo Shanghai Jiao Tong University
  • Hongyuan Zha Georgia Institute of Technology and East China Normal University
  • Yi Xu Shanghai Jiao Tong University




Dictionary learning, Group-graph structures, Mutually reinforcing, Sparse Representation, Classification


In this paper, we propose a novel dictionary learning method in the semi-supervised setting by dynamically coupling graph and group structures. To this end, samples are represented by sparse codes inheriting their graph structure while the labeled samples within the same class are represented with group sparsity, sharing the same atoms of the dictionary. Instead of statically combining graph and group structures, we take advantage of them in a mutually reinforcing way — in the dictionary learning phase, we introduce the unlabeled samples into groups by an entropy-based method and then update the corresponding local graph, resulting in a more structured and discriminative dictionary. We analyze the relationship between the two structures and prove the convergence of our proposed method. Focusing on image classification task, we evaluate our approach on several datasets and obtain superior performance compared with the state-of-the-art methods, especially in the case of only a few labeled samples and limited dictionary size.




How to Cite

Xu, H., Yu, L., Luo, D., Zha, H., & Xu, Y. (2015). Dictionary Learning with Mutually Reinforcing Group-Graph Structures. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9570



Main Track: Novel Machine Learning Algorithms