Unleash the Power of Label Space: Label Enhancement for Label Distribution Learning
Xin Geng · IEEE/WIC/ACM International Conference on Web Intelligence · 2021
In the existing machine learning literature, the labels of the training examples are usually just used in the calculation of loss. Most sophisticated operations are actually conducted on the instances, such as feature extraction, feature selection, manifold embedding, dimensionality reduction, etc. Researchers take obviously more efforts in the feature space than in the label space, which is not strange since labels are traditionally represented by logical values, i.e., 1 if the label is relevant to the instance and 0 otherwise. However, if we can somehow transform the logical label vectors into real-valued label vectors, then we can expect much more profound analysis in the label space.