Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35676
Title: Support vector guided dictionary learning
Authors: Cai, SJ
Zuo, WM
Zhang, L 
Feng, XC
Wang, P
Keywords: Dictionary learning
Support vector machine
Sparse representation
Fisher discrimination
Issue Date: 2014
Publisher: Springer
Source: In D Fleet, T Pajdla, B Schiele & T Tuytelaars (Eds.), Computer vision-- ECCV 2014 : 13th European Conference, Zurich, Switzerland, September 6-12, 2014 : proceedings, p. 624-639. Cham : Springer, 2014 How to cite?
Series/Report no.: Lecture Notes in Computer Science, v. 8692
Abstract: Discriminative dictionary learning aims to learn a dictionary from training samples to enhance the discriminative capability of their coding vectors. Several discrimination terms have been proposed by assessing the prediction loss (e.g., logistic regression) or class separation criterion (e.g., Fisher discrimination criterion) on the coding vectors. In this paper, we provide a new insight on discriminative dictionary learning. Specifically, we formulate the discrimination term as the weighted summation of the squared distances between all pairs of coding vectors. The discrimination term in the state-of-the-art Fisher discrimination dictionary learning (FDDL) method can be explained as a special case of our model, where the weights are simply determined by the numbers of samples of each class. We then propose a parameterization method to adaptively determine the weight of each coding vector pair, which leads to a support vector guided dictionary learning (SVGDL) model. Compared with FDDL, SVGDL can adaptively assign different weights to different pairs of coding vectors. More importantly, SVGDL automatically selects only a few critical pairs to assign non-zero weights, resulting in better generalization ability for pattern recognition tasks. The experimental results on a series of benchmark databases show that SVGDL outperforms many state-of-the-art discriminative dictionary learning methods.
URI: http://hdl.handle.net/10397/35676
ISBN: 978-3-319-10593-2 (online)
978-3-319-10592-5 (print)
ISSN: 0302-9743
DOI: 10.1007/978-3-319-10593-2_41
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