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label-noise robust classification with multi-view learning

Science China(Technological Sciences) 2023年第6期66卷 1841-1854页

作者： LIANG NaiYao YANG ZuYuan LI LingJiang LI ZhenNi XIE ShengLi Guangdong Key Laboratory of IoT Information Technology School of AutomationGuangdong University of TechnologyGuangzhou 510006China Key Laboratory of iDetection and Manufacturing-IoT Ministry of EducationGuangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

label noise is often contained in the training data due to various human factors or measurement errors,which significantly causes a negative effect on *** many previous methods that have been proposed to learn robust classifiers,they are mainly based on the single-view *** the other hand,although existing multi-view classification methods benefit from the more comprehensive information,they rarely consider label *** this paper,we propose a novel label-noise robust classification model with multi-view learning to overcome these *** the proposed model,not only the classifier learning but also the label-noise removal can benefit from the multi-view ***,we relax the label matrix of the basic multi-view least squares regression model,and develop a nonlinear transformation with a natural probabilistic approximation in the process of labels,which is conveniently optimized and beneficial to improve the discriminative ability of ***,we preserve the intrinsic manifold structure of multi-view data on the relaxed label matrix,facilitating the process of label *** optimizing the proposed model with the nonlinear transformation,we derive a lemma about the partial derivation of the softmax related function,and develop an efficient alternating *** evaluations on six real-world datasets confirm the advantages of the proposed method,compared to the related state-of-the-art methods.

关键词： label noise multi-view learning classification robust least squares regression label relaxation

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