Sparse and redundant representation modeling-What next?

Research output: Contribution to journalArticlepeer-review


Signal processing relies heavily on data models; these are mathematical constructions imposed on the data source that force a dimensionality reduction of some sort. The vast activity in signal processing during the past decades is essentially driven by an evolution of these models and their use in practice. In that respect, the past decade has been certainly the era of sparse and redundant representations, a popular and highly effective data model. This very appealing model led to a long series of intriguing theoretical and numerical questions, and to many innovative ideas that harness this model to real engineering problems. The new entries recently added to the IEEE-SPL EDICS reflect the popularity of this model and its impact on signal processing research and practice.

Original languageEnglish
Article number6329933
Pages (from-to)922-928
Number of pages7
JournalIEEE Signal Processing Letters
Issue number12
StatePublished - 2012


  • Data models
  • dimensionality reduction
  • projection
  • pursuit
  • sparse and redundant representations

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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