TY - GEN
T1 - A unified framework for generalized linear discriminant analysis
AU - Ji, Shuiwang
AU - Ye, Jieping
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - Linear Discriminant Analysis (LDA) is one of the wellknown methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ various techniques to deal with the singularity problem, which occurs when the data dimensionality is larger than the sample size. They have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed via a transfer function. The proposed framework elucidates the properties of various algorithms and their relationships. Based on the presented analysis, we propose an efficient model selection algorithm for LDA. We conduct extensive experiments using a collection of high-dimensional data, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.
AB - Linear Discriminant Analysis (LDA) is one of the wellknown methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ various techniques to deal with the singularity problem, which occurs when the data dimensionality is larger than the sample size. They have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed via a transfer function. The proposed framework elucidates the properties of various algorithms and their relationships. Based on the presented analysis, we propose an efficient model selection algorithm for LDA. We conduct extensive experiments using a collection of high-dimensional data, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.
UR - http://www.scopus.com/inward/record.url?scp=51949110421&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51949110421&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2008.4587377
DO - 10.1109/CVPR.2008.4587377
M3 - Conference contribution
AN - SCOPUS:51949110421
SN - 9781424422432
T3 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
BT - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Y2 - 23 June 2008 through 28 June 2008
ER -