Abstract
Label distribution learning (LDL) can be viewed
as the generalization of multi-label learning. This
novel paradigm focuses on the relative importance
of different labels to a particular instance. Most
previous LDL methods either ignore the correlation
among labels, or only exploit the label correlations
in a global way. In this paper, we utilize both the
global and local relevance among labels to provide
more information for training model and propose a
novel label distribution learning algorithm. In particular, a label correlation matrix based on low-rank
approximation is applied to capture the global label correlations. In addition, the label correlation
among local samples are adopted to modify the label correlation matrix. The experimental results on
real-world data sets show that the proposed algorithm outperforms state-of-the-art LDL methods