  Label distribution learning (LDL) is an effective method to predict the label
description degree (a.k.a. label distribution) of a sample. However, annotating
label distribution (LD) for training samples is extremely costly. So recent
studies often first use label enhancement (LE) to generate the estimated label
distribution from the logical label and then apply external LDL algorithms on
the recovered label distribution to predict the label distribution for unseen
samples. But this step-wise manner overlooks the possible connections between
LE and LDL. Moreover, the existing LE approaches may assign some description
degrees to invalid labels. To solve the above problems, we propose a novel
method to learn an LDL model directly from the logical label, which unifies LE
and LDL into a joint model, and avoids the drawbacks of the previous LE
methods. Extensive experiments on various datasets prove that the proposed
approach can construct a reliable LDL model directly from the logical label,
and produce more accurate label distribution than the state-of-the-art LE
methods.
