TY - GEN
T1 - Discriminant analysis
T2 - 5th IEEE International Conference on Data Mining, ICDM 2005
AU - Zhang, Peng
AU - Peng, Jing
AU - Riedel, Norbert
PY - 2005
Y1 - 2005
N2 - Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and machine learning. It however suffers from the small sample size (SSS) problem when data dimensionality is greater than the sample size. Many modified methods have been proposed to address some aspect of this difficulty from a particular viewpoint. A. comprehensive framework that provides a complete solution to the SSS problem is still missing. In this paper, we provide a unified approach to LDA, and investigate the SSS problem in the framework of statistical learning theory. In such a unified approach, our analysis results in a deeper understanding of LDA. We demonstrate that LDA (and its nonlinear extension) belongs to the same framework -where powerful classifiers such as support vector machines (SVMs) are formulated. In addition, this approach allows us to establish an error bound for LDA. Finally our experiments validate our theoretical analysis results.
AB - Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and machine learning. It however suffers from the small sample size (SSS) problem when data dimensionality is greater than the sample size. Many modified methods have been proposed to address some aspect of this difficulty from a particular viewpoint. A. comprehensive framework that provides a complete solution to the SSS problem is still missing. In this paper, we provide a unified approach to LDA, and investigate the SSS problem in the framework of statistical learning theory. In such a unified approach, our analysis results in a deeper understanding of LDA. We demonstrate that LDA (and its nonlinear extension) belongs to the same framework -where powerful classifiers such as support vector machines (SVMs) are formulated. In addition, this approach allows us to establish an error bound for LDA. Finally our experiments validate our theoretical analysis results.
UR - http://www.scopus.com/inward/record.url?scp=34548588868&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2005.51
DO - 10.1109/ICDM.2005.51
M3 - Conference contribution
AN - SCOPUS:34548588868
SN - 0769522785
SN - 9780769522784
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 514
EP - 521
BT - Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Y2 - 27 November 2005 through 30 November 2005
ER -