Tractable Learning of Sparsely Used Dictionaries from Incomplete Samples

Thanh V. Nguyen, Akshay Soni, Chinmay Hegde

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

    Abstract

    In dictionary learning, we seek a collection of atoms that sparsely represent a given set of training samples. While this problem is well-studied, relatively less is known about the more challenging case where the samples are incomplete, i.e., we only observe a fraction of their coordinates. In this paper, we develop and analyze an algorithm to solve this problem, provided that the dictionary satisfies additional low-dimensional structure.

    Original languageEnglish (US)
    Title of host publication2019 13th International Conference on Sampling Theory and Applications, SampTA 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728137414
    DOIs
    StatePublished - Jul 2019
    Event13th International Conference on Sampling Theory and Applications, SampTA 2019 - Bordeaux, France
    Duration: Jul 8 2019Jul 12 2019

    Publication series

    Name2019 13th International Conference on Sampling Theory and Applications, SampTA 2019

    Conference

    Conference13th International Conference on Sampling Theory and Applications, SampTA 2019
    CountryFrance
    CityBordeaux
    Period7/8/197/12/19

    ASJC Scopus subject areas

    • Statistics and Probability
    • Signal Processing
    • Analysis
    • Statistics, Probability and Uncertainty
    • Applied Mathematics

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  • Cite this

    Nguyen, T. V., Soni, A., & Hegde, C. (2019). Tractable Learning of Sparsely Used Dictionaries from Incomplete Samples. In 2019 13th International Conference on Sampling Theory and Applications, SampTA 2019 [9030979] (2019 13th International Conference on Sampling Theory and Applications, SampTA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SampTA45681.2019.9030979