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Dimensionality reduction by minimal distance maximization

Publication ,  Conference
Xu, B; Huang, K; Liu, CL
Published in: Proceedings - International Conference on Pattern Recognition
November 18, 2010

In this paper, we propose a novel discriminant analysis method, called Minimal Distance Maximization (MDM). In contrast to the traditional LDA, which actually maximizes the average divergence among classes, MDM attempts to find a low-dimensional subspace that maximizes the minimal (worst-case) divergence among classes. This "minimal" setting solves the problem caused by the "average" setting of LDA that tends to merge similar classes with smaller divergence when used for multi-class data. Furthermore, we elegantly formulate the worst-case problem as a convex problem, making the algorithm solvable for larger data sets. Experimental results demonstrate the advantages of our proposed method against five other competitive approaches on one synthetic and six real-life data sets. © 2010 IEEE.

Duke Scholars

Published In

Proceedings - International Conference on Pattern Recognition

DOI

ISSN

1051-4651

ISBN

9780769541099

Publication Date

November 18, 2010

Start / End Page

569 / 572
 

Citation

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Xu, B., Huang, K., & Liu, C. L. (2010). Dimensionality reduction by minimal distance maximization. In Proceedings - International Conference on Pattern Recognition (pp. 569–572). https://doi.org/10.1109/ICPR.2010.144
Xu, B., K. Huang, and C. L. Liu. “Dimensionality reduction by minimal distance maximization.” In Proceedings - International Conference on Pattern Recognition, 569–72, 2010. https://doi.org/10.1109/ICPR.2010.144.
Xu B, Huang K, Liu CL. Dimensionality reduction by minimal distance maximization. In: Proceedings - International Conference on Pattern Recognition. 2010. p. 569–72.
Xu, B., et al. “Dimensionality reduction by minimal distance maximization.” Proceedings - International Conference on Pattern Recognition, 2010, pp. 569–72. Scopus, doi:10.1109/ICPR.2010.144.
Xu B, Huang K, Liu CL. Dimensionality reduction by minimal distance maximization. Proceedings - International Conference on Pattern Recognition. 2010. p. 569–572.

Published In

Proceedings - International Conference on Pattern Recognition

DOI

ISSN

1051-4651

ISBN

9780769541099

Publication Date

November 18, 2010

Start / End Page

569 / 572