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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.

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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

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Chen, Zhe
Wu, Xiao-Jun
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

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