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Discriminative Orthonormal Dictionary Learning for Fast Low-Rank Representation

  • Zhen DongEmail author
  • Mingtao Pei
  • Yunde Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)

Abstract

This paper presents a discriminative orthonormal dictionary learning method for low-rank representation. The orthonormal property is beneficial for the representative power of the dictionary by avoiding the dictionary redundancy. To enhance the discriminative power of the dictionary, all the class-specific dictionaries which are encouraged to well represent the samples from the same class are optimized simultaneously. With the learned discriminative orthonormal dictionary, the low-rank representation problem can be solved much faster than traditional methods. Experiments on three public datasets demonstrate the effectiveness and efficiency of our method.

Keywords

Discriminative dictionary learning Orthonormal Fast low-rank representation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Beijing Laboratory of Intelligent Information Technology, School of Computer ScienceBeijing Institute of TechnologyBeijingPeople’s Republic of China

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