Support vector guided dictionary learning

Sijia Cai, Wangmeng Zuo, Lei Zhang, Xiangchu Feng, Ping Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

101 Citations (Scopus)


Discriminative dictionary learning aims to learn a dictionary from training samples to enhance the discriminative capability of their coding vectors. Several discrimination terms have been proposed by assessing the prediction loss (e.g., logistic regression) or class separation criterion (e.g., Fisher discrimination criterion) on the coding vectors. In this paper, we provide a new insight on discriminative dictionary learning. Specifically, we formulate the discrimination term as the weighted summation of the squared distances between all pairs of coding vectors. The discrimination term in the state-of-the-art Fisher discrimination dictionary learning (FDDL) method can be explained as a special case of our model, where the weights are simply determined by the numbers of samples of each class. We then propose a parameterization method to adaptively determine the weight of each coding vector pair, which leads to a support vector guided dictionary learning (SVGDL) model. Compared with FDDL, SVGDL can adaptively assign different weights to different pairs of coding vectors. More importantly, SVGDL automatically selects only a few critical pairs to assign non-zero weights, resulting in better generalization ability for pattern recognition tasks. The experimental results on a series of benchmark databases show that SVGDL outperforms many state-of-the-art discriminative dictionary learning methods.
Original languageEnglish
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
PublisherSpringer Verlag
Number of pages16
EditionPART 4
ISBN (Print)9783319105925
Publication statusPublished - 1 Jan 2014
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: 6 Sept 201412 Sept 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume8692 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th European Conference on Computer Vision, ECCV 2014


  • Dictionary learning
  • Fisher discrimination
  • sparse representation
  • support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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