局部协同视角下的鲁棒标记分布学习

doi:10.16451/j.cnki.issn1003-6059.202101005

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Abstract In the most of label distribution learning(LDL)algorithms, the correlations among different labels and the overall structure of label distribution are destroyed to a certain extent. Moreover, most existing LDL algorithms mainly focus on improving the predictive performance of label distribution, while ignoring the significance of computational cost and noise robustness in practical applications. To tackle these issues, a local collaborative representation based label distribution learning algorithm (LCR-LDL) is proposed. In LCR-LDL, an unlabeled sample is treated as a collaborative representation of the local dictionary constructed by the neighborhood of the unlabeled sample, and the discriminating information of representation coefficients is utilized to reconstruct the label distribution of unlabeled sample. Experimental results on 15 real-world LDL datasets show that LCR-LDL effectively improves the predictive performance for LDL tasks with a better robustness and low computational cost.

Key words： Label Distribution Learning(LDL) Multi-label Learning Label Ambiguity Sparse Dictionary Learning Robustness

Received: 15 June 2020

ZTFLH:

TP 181

Fund:National Natural Science Foundation of China(No.61672276,51975294,62006128,62076111)

Corresponding Authors: SHANG Lin, Ph.D., associate professor. Her research interests include machine learning, data mining and computational intelligence.

About author:: XU Suping, Ph.D. candidate. His research interests include machine learning, data mi-ning and computational intelligence.
ZHOU Yujie, master student. His research interests include deep learning, image caption and image classification.

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Cite this article:

XU Suping,SHANG Lin,ZHOU Yujie. Robust Label Distribution Learning from a Perspective of Local Collaboration[J]. , 2021, 34(1): 44-57.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202101005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I1/44

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