Online sparsifying transform learning for signal processing

Saiprasad Ravishankar, Bihan Weni, Yoram Bresler

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

Abstract

Many techniques in signal and image processing exploit the sparsity of natural signals in a transform domain or dictionary. Adaptive synthesis dictionaries have been shown to be useful in applications such as signal denoising, and compressed sensing. More recently, the data-driven adaptation of sparsifying transforms has received some interest. The sparsifying transform model allows for exact and cheap computations. In this work, we propose a framework for online learning of square sparsifying transforms. Such online learning can be particularly useful when dealing with big data, and for signal processing applications such as realtime sparse representation and denoising. The proposed online transform learning algorithm is shown to have a much lower computational cost than online synthesis dictionary learning. The sequential learning of a sparsifying transform also typically converges faster than batch mode transform learning. Preliminary experiments show the usefulness of the proposed schemes for sparse representation, and denoising.

Original languageEnglish (US)
Title of host publication2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages364-368
Number of pages5
ISBN (Electronic)9781479970889
DOIs
StatePublished - Feb 5 2014
Event2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States
Duration: Dec 3 2014Dec 5 2014

Publication series

Name2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014

Other

Other2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
CountryUnited States
CityAtlanta
Period12/3/1412/5/14

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Keywords

  • Big data
  • Denoising
  • Dictionary learning
  • Online learning
  • Sparse representations
  • Sparsifying transforms

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems

Cite this

Ravishankar, S., Weni, B., & Bresler, Y. (2014). Online sparsifying transform learning for signal processing. In 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 (pp. 364-368). [7032140] (2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2014.7032140