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.
|Title of host publication||Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007|
|Number of pages||6|
|Publication status||Published - 1 Dec 2007|
|Event||7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States|
Duration: 28 Oct 2007 → 31 Oct 2007
|Other||7th IEEE International Conference on Data Mining, ICDM 2007|
|Period||28/10/07 → 31/10/07|