基于低秩表示的标记分布学习算法

doi:10.16451/j.cnki.issn1003-6059.202102006

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Abstract Label correlations, noises and corruptions are ignored in label distribution learning algorithms. Aiming at this problem, a label distribution learning method based on low-rank representation(LDL-LRR)is proposed. The base of the feature space is leveraged to represent the sample information, and consequently dimensionality reduction of the data in the original feature space is achieved. To capture the global structure of the data, low-rank representation is transferred to the label space to impose low-rank constraint to the model. Augmented Lagrange method and quasi-Newton method are employed to solve the LRR and objective function, respectively. Finally, the label distribution is predicted by the maximum entropy model. Experiments on 10 datasets show that LDL-LRR produces good performance and stable effect.

Key words： Label Ambiguity Single-Label Learning Multi-label Learning(MLL) Label Distribution Learning(LDL) Low-Rank Representation(LRR)

Received: 03 August 2020

ZTFLH:

TP 181

Corresponding Authors: LI Chen, Ph.D., lec-turer. Her research interests include multi-media technology and pattern recognition.

About author:: LIU Ruixin, master student. Her research interests include machine learning.LIU Xinyuan, Ph.D. candidate. Her research interests include machine learning.

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LIU Ruixin,LIU Xinyuan,LI Chen. Label Distribution Learning Method Based on Low-Rank Representation[J]. , 2021, 34(2): 146-156.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202102006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I2/146

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