Abstract
Label distribution learning (LDL) is a newly arisen
learning paradigm to deal with label ambiguity
problems, which can explore the relative importance of different labels in the description of a particular instance. Although some existing LDL algorithms have achieved better effectiveness in real applications, most of them typically emphasize on improving the learning ability by manipulating the label space, while ignoring the fact that irrelevant and
redundant features exist in most practical classifi-
cation learning tasks, which increase not only storage requirements but also computational overheads. Furthermore, noises in data acquisition will bring
negative effects on the generalization performance
of LDL algorithms. In this paper, we propose a
novel algorithm, i.e., Latent Semantics Encoding
for Label Distribution Learning (LSE-LDL), which
learns the label distribution and implements feature
selection simultaneously under the guidance of latent semantics. Specifically, to alleviate noise disturbances, we seek and encode discriminative original physical/chemical features into advanced latent semantic features, and then construct a mapping
from the encoded semantic space to the label space
via empirical risk minimization. Empirical studies
on 15 real-world data sets validate the effectiveness
of the proposed algorithm