Document Type
Conference Proceeding
Publication Date
12-1-2007
Journal / Book Title
Seventh IEEE International Conference on Data Mining (ICDM 2007)
Abstract
Linear discriminant analysis (LDA) for dimension reduction has been applied to a wide variety of face recognition tasks. However, it has two major problems. First, it suffers from the small sample size problem when dimensionality is greater than the sample size. Second, it creates subspaces that favor well separated classes over those that are not. In this paper, we propose a simple weighted criterion for linear dimension reduction that addresses the above two problems associated with LDA. In addition, there are well established numerical procedures such as semi-definite programming for efficiently computing the proposed criterion. We demonstrate the efficacy of our proposal and compare it against other competing techniques using a number of examples.
DOI
10.1109/ICDM.2007.81
Journal ISSN / Book ISBN
Electronic ISSN: 2374-8486
Montclair State University Digital Commons Citation
Peng, Jing and Robila, Stefan, "Weighted Additive Criterion for Linear Dimension Reduction" (2007). Department of Computer Science Faculty Scholarship and Creative Works. 628.
https://digitalcommons.montclair.edu/compusci-facpubs/628
Published Citation
Peng, J., & Robila, S. (2007). Weighted additive criterion for linear dimension reduction. In Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007 (pp. 619-624). Article 4470300 (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2007.81
