Abstract
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.
Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004 |
Publisher | Neural information processing systems foundation |
ISBN (Print) | 0262195348, 9780262195348 |
State | Published - 2005 |
Event | 18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada Duration: Dec 13 2004 → Dec 16 2004 |
Publication series
Name | Advances in Neural Information Processing Systems |
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ISSN (Print) | 1049-5258 |
Other
Other | 18th Annual Conference on Neural Information Processing Systems, NIPS 2004 |
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Country/Territory | Canada |
City | Vancouver, BC |
Period | 12/13/04 → 12/16/04 |
Bibliographical note
Copyright:Copyright 2014 Elsevier B.V., All rights reserved.