  Recently, label distribution learning (LDL) has drawn much attention in
machine learning, where LDL model is learned from labelel instances. Different
from single-label and multi-label annotations, label distributions describe the
instance by multiple labels with different intensities and accommodate to more
general scenes. Since most existing machine learning datasets merely provide
logical labels, label distributions are unavailable in many real-world
applications. To handle this problem, we propose two novel label enhancement
methods, i.e., Label Enhancement with Sample Correlations (LESC) and
generalized Label Enhancement with Sample Correlations (gLESC). More
specifically, LESC employs a low-rank representation of samples in the feature
space, and gLESC leverages a tensor multi-rank minimization to further
investigate the sample correlations in both the feature space and label space.
Benefitting from the sample correlations, the proposed methods can boost the
performance of label enhancement. Extensive experiments on 14 benchmark
datasets demonstrate the effectiveness and superiority of our methods.
