Deep Cross-View Label Embedding with Correlation and Structure Preserved for Multi-Label Classification

Abstract

Label embedding is an important family of multi-label classification algorithms which can jointly extract the information of all labels for better performance. However, few works have been done on label embedding methods which consider the structure information of original feature and label space simultaneously. We propose a novel deep neural network (DNN) based model for learning an effective deep latent space, namely Deep Cross-view label space Embedding with Correlation and Structure preserved (DCECS). In DCECS, the latent space correlates with feature and label spaces closely by virtue of the deep cross-view embedding. Meanwhile, the latent space is also learned under the guidance of label correlation and local structure of feature space which are exploited by hypergraph and graph regularizations. The overall framework achieves the complementarity and correspondence between information of feature and label space, therefore the feature-aware deep latent space we learned has strong predictability and discriminant ability. Extensive experimental results on datasets with many labels demonstrate that our proposed approach is significantly better than the existing label embedding algorithms.