Label Distribution Learning by Regularized Sample Self-Representation

Abstract

Multilabel learning that focuses on an instance of the corresponding related or unrelated label can solve many ambiguity problems. Label distribution learning (LDL) reflects the importance of the related label to an instance and offers a more general learning framework than multilabel learning. However, the current LDL algorithms ignore the linear relationship between the distribution of labels and the feature. In this paper, we propose a regularized sample self-representation (RSSR) approach for LDL. First, the label distribution problem is formalized by sample self-representation, whereby each label distribution can be represented as a linear combination of its relevant features. Second, the LDL problem is solved by 2 -norm least-squares and 2,1 -norm least-squares methods to reduce the effects of outliers and overfitting. The corresponding algorithms are named RSSR-LDL2 and RSSR-LDL21. Third, the proposed algorithms are compared with four state-of-the-art LDL algorithms using 12 public datasets and five evaluation metrics. The results demonstrate that the proposed algorithms can effectively identify the predictive label distribution and exhibit good performance in terms of distance and similarity evaluations.

In-Depth Research and Analysis of Multilabel Learning Algorithm

“…The difference between the various algorithms based on graph is the defining on the estimated function. Graph-based concept is very clear, but its large storage is overhead and difficult to use on large-scale data [32,33].…”