Label Embedding for Multi-label Classification Via Dependence Maximization

Multi-label classification has aroused extensive attention in various fields. With the emergence of high-dimensional label space, academia has devoted to performing label embedding in recent years. Whereas current embedding approaches do not take feature space correlation sufficiently into consideration or require an encoding function while learning embedded space. Besides, few of them can be spread to track the missing labels. In this paper, we propose a Label Embedding method via Dependence Maximization (LEDM), which obtains the latent space on which the label and feature information can be embedded simultaneously. To end this, the low-rank factorization model on the label matrix is applied to exploit label correlations instead of the encoding process. The dependence between feature space and label space is increased by the Hilbert-Schmidt independence criterion to facilitate the predictability. The proposed LEDM can be easily extended the missing labels in learning embedded space at the same time. Comprehensive experimental results on data sets validate the effectiveness of our approach over the state-of-art methods on both complete-label and missing-label cases.