Learning dictionaries with graph embedding constraints

Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias

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

11 Scopus citations


Several supervised, semi-supervised, and unsupervised machine learning schemes can be unified under the general framework of graph embedding. Incorporating graph embedding principles into sparse representation based learning schemes can provide an improved performance in several learning tasks. In this work, we propose a dictionary learning procedure for computing discriminative sparse codes that obey graph embedding constraints. In order to compute the graph-embedded sparse codes, we integrate a modified version of the sequential quadratic programming procedure with the feature sign search method. We demonstrate, using simulations with the AR face database, that the proposed approach performs better than several baseline methods in supervised and semi-supervised classification.

Original languageEnglish (US)
Title of host publicationConference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Number of pages5
StatePublished - 2012
Event46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States
Duration: Nov 4 2012Nov 7 2012

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Country/TerritoryUnited States
CityPacific Grove, CA

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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