Label Distribution Learning Machine
Jing Wang,u00a0Xin Geng
Although Label Distribution Learning (LDL) has witnessed extensive classification applications, it faces the challenge of objective mismatch u2013 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 extbf{L}abel extbf{D}istribution extbf{L}earning extbf{M}achine (LDLM). First, we define the label distribution margin and propose the extbf{S}upport extbf{V}ector extbf{R}egression extbf{M}achine (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.
