MoRE: Multi-output residual embedding for multi-label classification

Abstract

Multi-label classification (MLC) is one of the challenging tasks in computer vision, where it confronts high dimensional problem both in output label and input feature spaces. This paper proposed solving MLC through multi-output residual embedding (MoRE), which learns appropriate distance metric by analyzing the residuals between input and output spaces. Unlike traditional MLC paradigms that learn relationships between label space and feature space, our proposed approach further learns a low-rank structure in residuals between input and output spaces. And it encodes such residual projection to achieve dimension reduction in label space, enhancing the performance of the proposed algorithm in processing high dimensional MLC task. Furthermore, considering the label correlations between instances and its neighbors, multiple residuals of instances neighbors are also incorporated into the proposed model to further learn more appropriate distance metric in the same way. Overall, with residual embedding learning from instances and their neighbors, the obtained metric can learn a more appropriate low-rank structure in label space to handle high dimensional problem in MLC. Experimental results on several data sets, such as Cal500, Corel5k, Bibtex, Delicious, Tmc2007, 20ng, Mirflickr and Rcv1s1, demonstrate the excellent predictive performance of MoRE among STOA methods, such as LMMO-kNN, M3MDC, KRAM, SEEM, CPLST, CSSP, FaIE.