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.
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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2021, Vol. 34 
