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Maxi-Min discriminant analysis via online learning.

Publication ,  Journal Article
Xu, B; Huang, K; Liu, C-L
Published in: Neural networks : the official journal of the International Neural Network Society
October 2012

Linear Discriminant Analysis (LDA) is an important dimensionality reduction algorithm, but its performance is usually limited on multi-class data. Such limitation is incurred by the fact that LDA actually maximizes the average divergence among classes, whereby similar classes with smaller divergence tend to be merged in the subspace. To address this problem, we propose a novel dimensionality reduction method called Maxi-Min Discriminant Analysis (MMDA). In contrast to the traditional LDA, MMDA attempts to find a low-dimensional subspace by maximizing the minimal (worst-case) divergence among classes. This "minimal" setting overcomes the problem of LDA that tends to merge similar classes with smaller divergence when used for multi-class data. We formulate MMDA as a convex problem and further as a large-margin learning problem. One key contribution is that we design an efficient online learning algorithm to solve the involved problem, making the proposed method applicable to large scale data. Experimental results on various datasets demonstrate the efficiency and the efficacy of our proposed method against five other competitive approaches, and the scalability to the data with thousands of classes.

Duke Scholars

Published In

Neural networks : the official journal of the International Neural Network Society

DOI

EISSN

1879-2782

ISSN

0893-6080

Publication Date

October 2012

Volume

34

Start / End Page

56 / 64

Related Subject Headings

  • Pattern Recognition, Automated
  • Discrimination Learning
  • Discriminant Analysis
  • Artificial Intelligence & Image Processing
  • Artificial Intelligence
  • Algorithms
  • 4905 Statistics
  • 4611 Machine learning
  • 4602 Artificial intelligence
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Xu, B., Huang, K., & Liu, C.-L. (2012). Maxi-Min discriminant analysis via online learning. Neural Networks : The Official Journal of the International Neural Network Society, 34, 56–64. https://doi.org/10.1016/j.neunet.2012.06.001
Xu, Bo, Kaizhu Huang, and Cheng-Lin Liu. “Maxi-Min discriminant analysis via online learning.Neural Networks : The Official Journal of the International Neural Network Society 34 (October 2012): 56–64. https://doi.org/10.1016/j.neunet.2012.06.001.
Xu B, Huang K, Liu C-L. Maxi-Min discriminant analysis via online learning. Neural networks : the official journal of the International Neural Network Society. 2012 Oct;34:56–64.
Xu, Bo, et al. “Maxi-Min discriminant analysis via online learning.Neural Networks : The Official Journal of the International Neural Network Society, vol. 34, Oct. 2012, pp. 56–64. Epmc, doi:10.1016/j.neunet.2012.06.001.
Xu B, Huang K, Liu C-L. Maxi-Min discriminant analysis via online learning. Neural networks : the official journal of the International Neural Network Society. 2012 Oct;34:56–64.
Journal cover image

Published In

Neural networks : the official journal of the International Neural Network Society

DOI

EISSN

1879-2782

ISSN

0893-6080

Publication Date

October 2012

Volume

34

Start / End Page

56 / 64

Related Subject Headings

  • Pattern Recognition, Automated
  • Discrimination Learning
  • Discriminant Analysis
  • Artificial Intelligence & Image Processing
  • Artificial Intelligence
  • Algorithms
  • 4905 Statistics
  • 4611 Machine learning
  • 4602 Artificial intelligence