An Overview of Label Space Dimension Reduction for Multi-Label Classification

Abstract

Multi-label classification with many labels are common in real-world application. However, traditional multi-label classifiers often become computationally inefficient for hundreds or even thousands of labels. Therefore, the label space dimension reduction is designed to address this problem. In this paper, the existing studies of label space dimension reduction are summarized; especially, these studies were classified into two categories: label space dimension reduction based on transformed labels and label subset; meanwhile, we analyze the studies belonging to each type and give the experimental comparison of two typical LSDR algorithms. To the best of our knowledge, this is the first effort to review the development of label space dimension reduction.