Robust dictionary learning by error source decomposition

Zhuoyuan Chen, Ying Wu

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

18 Scopus citations


Sparsity models have recently shown great promise in many vision tasks. Using a learned dictionary in sparsity models can in general outperform predefined bases in clean data. In practice, both training and testing data may be corrupted and contain noises and outliers. Although recent studies attempted to cope with corrupted data and achieved encouraging results in testing phase, how to handle corruption in training phase still remains a very difficult problem. In contrast to most existing methods that learn the dictionary from clean data, this paper is targeted at handling corruptions and outliers in training data for dictionary learning. We propose a general method to decompose the reconstructive residual into two components: a non-sparse component for small universal noises and a sparse component for large outliers, respectively. In addition, further analysis reveals the connection between our approach and the "partial" dictionary learning approach, updating only part of the prototypes (or informative code words) with remaining (or noisy code words) fixed. Experiments on synthetic data as well as real applications have shown satisfactory performance of this new robust dictionary learning approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9781479928392
StatePublished - 2013
Event2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
Duration: Dec 1 2013Dec 8 2013

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


Other2013 14th IEEE International Conference on Computer Vision, ICCV 2013
CitySydney, NSW


  • machine learning
  • robust statistics

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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