Group structured dirty dictionary learning for classification

Yuanming Suo, Minh Dao, Trac Tran, Hojjat Mousavi, Umamahesh Srinivas, Vishal Monga

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    19 Scopus citations

    Abstract

    Dictionary learning techniques have gained tremendous success in many classification problems. Inspired by the dirty model for multi-task regression problems, we proposed a novel method called group-structured dirty dictionary learning (GDDL) that incorporates the group structure (for each task) with the dirty model (across tasks) in the dictionary training process. Its benefits are two-fold: 1) the group structure enforces implicitly the label consistency needed between dictionary atoms and training data for classification; and 2) for each class, the dirty model separates the sparse coefficients into ones with shared support and unique support, with the first set being more discriminative. We use proximal operators and block coordinate decent to solve the optimization problem. GDDL has been shown to give state-of-art result on both synthetic simulation and two face recognition datasets.

    Original languageEnglish (US)
    Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages150-154
    Number of pages5
    ISBN (Electronic)9781479957514
    DOIs
    StatePublished - Jan 28 2014

    Publication series

    Name2014 IEEE International Conference on Image Processing, ICIP 2014

    All Science Journal Classification (ASJC) codes

    • Computer Vision and Pattern Recognition

    Fingerprint Dive into the research topics of 'Group structured dirty dictionary learning for classification'. Together they form a unique fingerprint.

  • Cite this

    Suo, Y., Dao, M., Tran, T., Mousavi, H., Srinivas, U., & Monga, V. (2014). Group structured dirty dictionary learning for classification. In 2014 IEEE International Conference on Image Processing, ICIP 2014 (pp. 150-154). [7025029] (2014 IEEE International Conference on Image Processing, ICIP 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIP.2014.7025029