Abstract
Label Distribution Learning (LDL) is a novel learning paradigm, aim of which is to minimize the distance between the model output and the groundtruth label distribution. We notice that, in real-word
applications, the learned label distribution model is
generally treated as a classification model, with the
label corresponding to the highest model output as
the predicted label, which unfortunately prompts an
inconsistency between the training phrase and the
test phrase. To solve the inconsistency, we propose
in this paper a new Label Distribution Learning
algorithm for Classification (LDL4C). Firstly, instead of KL-divergence, absolute loss is applied as
the measure for LDL4C. Secondly, samples are reweighted with information entropy. Thirdly, large
margin classifier is adapted to boost discrimination precision. We then reveal that theoretically
LDL4C seeks a balance between generalization and
discrimination. Finally, we compare LDL4C with
existing LDL algorithms on 17 real-word datasets,
and experimental results demonstrate the effectiveness of LDL4C in classification