Multi-label local discriminative embedding

作     者：Jujie Zhang Min Fang Huimin Chai 

作者机构：School of Computer Science and Technology Xidian University 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2017年第28卷第5期

页      面：1009-1018页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61472305) the Science Research Program,Xi’an,China(2017073CG/RC036CXDKD003) the Aeronautical Science Foundation of China(20151981009) 

主　　题：multi-label classification dimensionality reduction latent structure label correlation 

摘      要：Multi-label classification problems arise frequently in text categorization, and many other related applications. Like conventional categorization problems, multi-label categorization tasks suffer from the curse of high dimensionality. Existing multi-label dimensionality reduction methods mainly suffer from two limitations. First, latent nonlinear structures are not utilized in the input space. Second, the label information is not fully exploited. This paper proposes a new method, multi-label local discriminative embedding (MLDE), which exploits latent structures to minimize intraclass distances and maximize interclass distances on the basis of label correlations. The latent structures are extracted by constructing two sets of adjacency graphs to make use of nonlinear information. Non-symmetric label correlations, which are the case in real applications, are adopted. The problem is formulated into a global objective function and a linear mapping is achieved to solve out-of-sample problems. Empirical studies across 11 Yahoo sub-tasks, Enron and Bibtex are conducted to validate the superiority of MLDE to state-of-art multi-label dimensionality reduction methods.