Semi-supervised classification based on p-norm multiple kernel learning with manifold regularization  

作　　者：Tao Yang Dongmei Fu 

机构地区：[1]School of Automation and Electrical Engineering, University of Science & Technology Beijing

出　　处：《Journal of Systems Engineering and Electronics》2016年第6期1315-1325,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61272358)

摘　　要：Consider the efficiency of p-norm multiple kernel learning (MKL), which is extended to a semi-supervised learning (SSL) scenario by applying the manifold regularization technique. A manifold regularized p-norm multiple kernels model is constructed and applied to a semi-supervised classification task. Solutions are proposed for the case of p = 1, p > 1 and p = ∞, with an analysis of theorems and their proofs. In addition, experiments are conducted on several datasets using state-of-the-art methods to verify the efficiency of the proposed manifold regularized p-norm multiple kernels model in semi-supervised classification. © 2016 Beijing Institute of Aerospace Information.Consider the efficiency of p-norm multiple kernel learning (MKL), which is extended to a semi-supervised learning (SSL) scenario by applying the manifold regularization technique. A manifold regularized p-norm multiple kernels model is constructed and applied to a semi-supervised classification task. Solutions are proposed for the case of p = 1, p > 1 and p = ∞, with an analysis of theorems and their proofs. In addition, experiments are conducted on several datasets using state-of-the-art methods to verify the efficiency of the proposed manifold regularized p-norm multiple kernels model in semi-supervised classification. © 2016 Beijing Institute of Aerospace Information.

关 键 词：Classification (of information) EFFICIENCY 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]