Multi-view label embedding

Abstract

Multi-label classification has been successfully applied to image annotation, information retrieval, text categorization, etc. When the number of classes increases significantly, the traditional multi-label learning models will become computationally impractical. Label space dimension reduction (LSDR) is then developed to alleviate the effect of the high dimensionality of labels. However, almost all the existing LSDR methods focus on single-view learning. In this paper, we develop a multi-view label embedding (MVLE) model by exploiting the multi-view correlations. The label space and feature space of each view are bridged by a latent space. To exploit the consensus among different views, multi-view latent spaces are correlated by Hilbert–Schmidt independence criterion(HSIC). For a test sample, it is firstly embedded to the latent space of each view and then projected to the label space. The prediction is conducted by combining the multi-view outputs. Experiments on benchmark databases show that MVLE outperforms the state-of-the-art LSDR algorithms in both multi-view settings and different multi-view learning strategies.