Multi-label Quadruplet Dictionary Learning

Abstract

The explosion of the label space degrades the performance of the classic multi-class learning models. Label space dimension reduction (LSDR) is developed to reduce the dimension of the label space by learning a latent representation of both the feature space and label space. Almost all existing models adopt a two-step strategy, i.e., first learn the latent space, and then connect the feature space with the label space by the latent space. Additionally, the latent space lacks interpretability for LSDR. In this paper, motivated by cross-modal learning, we propose a novel one-step model, named Quadruplet Dictionary Learning (QDL), for multi-label classification with many labels. QDL models the latent space by the representation coefficients, which own preeminent recoverability, predictability and interpretability. By simultaneously learning two dictionary pairs, the feature space and label space are well bi-directly bridged and recovered by four dictionaries. Experiments on benchmark datasets show that QDL outperforms the state-of-the-art label space dimension reduction algorithms.