Label Distribution Learning Machine

Although Label Distribution Learning (LDL) has witnessed extensive classification applications, it faces the challenge of objective mismatch - the objective of LDL mismatches that of classification, which has seldom been noticed in existing studies. Our goal is to solve the objective mismatch and improve the classification performance of LDL. Specifically, we extend the margin theory to LDL and propose a new LDL method called Label Distribution Learning Machine (LDLM). First, we define the label distribution margin and propose the Support Vector Regression Machine (SVRM) to learn the optimal label. Second, we propose the adaptive margin loss to learn label description degrees. In theoretical analysis, we develop a generalization theory for the SVRM and analyze the generalization of LDLM. Experimental results validate the better classification performance of LDLM.