Abstract
Label distribution learning (LDL) assumes labels
can be associated to an instance to some degree,
thus it can learn the relevance of a label to a particular instance. Although LDL has got successful practical applications, one problem with existing LDL methods is that they are designed for data with complete supervised information, while in
reality, annotation information may be incomplete,
because assigning each label a real value to indicate
its association with a particular instance will result
in large cost in labor and time. In this paper, we will
solve LDL problem when given incomplete supervised information. We propose an objective based
on trace norm minimization to exploit the correlation between labels. We develop a proximal gradient descend algorithm and an algorithm based on
alternating direction method of multipliers. Experiments validate the effectiveness of our proposal