Correntropy with Nonnegative Constraint

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


Nonnegativity constraint is more consistent with the biological modeling of visual data and often leads to better performance for data representation and graph learning [66]. In this chapter, we present an overview of some recent advances in correntropy with nonnegative constraint. We begin with an introduction of an 1 regularized nonnegative sparse coding algorithm to learn a nonnegative sparse representation (NSR). Then we show how to use correntropy to learn a robust NSR. Finally, based on the divide and conquer strategy, a two-stage framework is discussed for large-scale sparse representation problems.


Recognition Rate Sparse Representation Sparse Code Full Column Rank High Recognition Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© The Author(s) 2014

Authors and Affiliations

  1. 1.National Laboratory of Pattern RecognitionInstitute of Automation Chinese Academy of SciencesBeijingChina
  2. 2.School of Information and ControlNanjing University of Information Science and TechnologyNanjingChina

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