Low-rank discriminative least squares regression for image classification
Chen, Zhe, Wu, Xiao-Jun and Kittler, Josef (2020) Low-rank discriminative least squares regression for image classification Signal Processing, 173, 107485.
Full text not available from this repository.Abstract
Discriminative least squares regression (DLSR) aims to learn relaxed regression labels to replace strict zero-one labels. However, the distance of the labels from the same class can also be enlarged while using the ε-draggings technique to force the labels of different classes to move in the opposite directions, and roughly persuing relaxed labels may lead to the problem of overfitting. To solve above problems, we propose a low-rank discriminative least squares regression model (LRDLSR) for multi-class image classification. Specifically, LRDLSR class-wisely imposes low-rank constraint on the relaxed labels obtained by non-negative relaxation matrix to improve its within-class compactness and similarity. Moreover, LRDLSR introduces an additional regularization term on the learned labels to avoid the problem of overfitting. We show that these two improvements help to learn a more discriminative projection for regression, thus achieving better classification performance. The experimental results over a range of image datasets demonstrate the effectiveness of the proposed LRDLSR method. The Matlab code of the proposed method is available at https://github.com/chenzhe207/LRDLSR.
Item Type: | Article | ||||||||||||
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Divisions : | Faculty of Engineering and Physical Sciences > Electronic Engineering | ||||||||||||
Authors : |
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Date : | 21 January 2020 | ||||||||||||
Funders : | National Natural Science Foundation of China, Ministry of Education of China, EPSRC | ||||||||||||
DOI : | 10.1016/j.sigpro.2020.107485 | ||||||||||||
Grant Title : | National Natural Science Foundation of China | ||||||||||||
Copyright Disclaimer : | © 2020 Elsevier B.V. All rights reserved. | ||||||||||||
Uncontrolled Keywords : | Discriminative least squares regression; Low-rank regression labels; Overfitting; Image classification; | ||||||||||||
Depositing User : | James Marshall | ||||||||||||
Date Deposited : | 09 Jul 2020 08:55 | ||||||||||||
Last Modified : | 09 Jul 2020 08:55 | ||||||||||||
URI: | http://epubs.surrey.ac.uk/id/eprint/858165 |
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