TY - GEN
T1 - Efficient kernel discriminant analysis via QR decomposition
AU - Xiong, Tao
AU - Ye, Jieping
AU - Li, Qi
AU - Cherkassky, Vladimir
AU - Janardan, Ravi
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Recently, a novel LDA algorithm based on QR Decomposition, namely LDA/QR, has been proposed, which is competitive in terms of classification accuracy with other LDA algorithms, but it has much lower costs in time and space. However, LDA/QR is based on linear projection, which may not be suitable for data with nonlinear structure. This paper first proposes an algorithm called KDA/QR, which extends the LDA/QR algorithm to deal with nonlinear data by using the kernel operator. Then an efficient approximation of KDA/QR called AKDA/QR is proposed. Experiments on face image data show that the classification accuracy of both KDA/QR and AKDA/QR are competitive with Generalized Discriminant Analysis (GDA), a general kernel discriminant analysis algorithm, while AKDA/QR has much lower time and space costs.
AB - Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Recently, a novel LDA algorithm based on QR Decomposition, namely LDA/QR, has been proposed, which is competitive in terms of classification accuracy with other LDA algorithms, but it has much lower costs in time and space. However, LDA/QR is based on linear projection, which may not be suitable for data with nonlinear structure. This paper first proposes an algorithm called KDA/QR, which extends the LDA/QR algorithm to deal with nonlinear data by using the kernel operator. Then an efficient approximation of KDA/QR called AKDA/QR is proposed. Experiments on face image data show that the classification accuracy of both KDA/QR and AKDA/QR are competitive with Generalized Discriminant Analysis (GDA), a general kernel discriminant analysis algorithm, while AKDA/QR has much lower time and space costs.
UR - http://www.scopus.com/inward/record.url?scp=84899008974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899008974&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84899008974
SN - 0262195348
SN - 9780262195348
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
PB - Neural information processing systems foundation
T2 - 18th Annual Conference on Neural Information Processing Systems, NIPS 2004
Y2 - 13 December 2004 through 16 December 2004
ER -