TY - GEN
T1 - Weighted additive criterion for linear dimension reduction
AU - Peng, Jing
AU - Robila, Stefan
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=49749091148&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2007.81
DO - 10.1109/ICDM.2007.81
M3 - Conference contribution
AN - SCOPUS:49749091148
SN - 0769530184
SN - 9780769530185
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 619
EP - 624
BT - Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
T2 - 7th IEEE International Conference on Data Mining, ICDM 2007
Y2 - 28 October 2007 through 31 October 2007
ER -