Label Distribution Learning Method Based on Low-Rank Representation

被引：0

作者：

Liu R. ^{[1
]}

Liu X. ^{[1
]}

Li C. ^{[1
]}

机构：

[1] School of Software Engineering, Xi'an Jiaotong University, Xi'an

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 02期

基金：

中国国家自然科学基金;

关键词：

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

D O I：

10.16451/j.cnki.issn1003-6059.202102006

中图分类号：

学科分类号：

摘要：

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. © 2021, Science Press. All right reserved.

引用

页码：146 / 156

页数：10

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