.png){kind=icon}
返回
Label-noise robust classification with multi-view learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s11431-021-2139-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Label noise is often contained in the training data due to various human factors or measurement errors, which significantly causes a negative effect on classifiers. Despite many previous methods that have been proposed to learn robust classifiers, they are mainly based on the single-view feature. On the other hand, although existing multi-view classification methods benefit from the more comprehensive information, they rarely consider label noise. In this paper, we propose a novel label-noise robust classification model with multi-view learning to overcome these limitations. In the proposed model, not only the classifier learning but also the label-noise removal can benefit from the multi-view information. Specifically, 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 classifiers. Moreover, we preserve the intrinsic manifold structure of multi-view data on the relaxed label matrix, facilitating the process of label relaxation. For 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 algorithm. Experimental evaluations on six real-world datasets confirm the advantages of the proposed method, compared to the related state-of-the-art methods.
Keyword:
label noise
multi-view learning
classification
robust
least squares regression
label relaxation
期刊
IF:
4.9
论文数:
5.0K
被引数:
9.9K
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容