Robust Cross-View Embedding With Discriminant Structure 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 to develop the multi-label embedding methods that can effectively deal with the interference of noisy data during training process. The noise often makes the labels of a few samples incorrect ( i.e. , missing or mislabeled), which could lead to a poor learning performance. To address this issue, we propose a novel cross-view based model. It performs a robust and discriminant embedding, namely Robust Cross-view Embedding with Discriminant Structure for Multi-label Classification (RCEDS). In RCEDS, a novel hypergraph fusion technique is designed to explore and utilize the complementary between the feature space and the label space to make the proposed RCEDS robust. Meanwhile, we use double-side metric learning to mine the consistency between the feature space and the label space to effectively improve the discriminative ability of our proposed RCEDS. Furthermore, we conduct a deep extension of RCEDS and effectively apply it to image annotation. Extensive experimental results on data sets with many labels demonstrate that our proposed approach can attain better classification performance than the existing label embedding algorithms.