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

被引：0

作者：

Tao Yang ^{[1
]}

Dongmei Fu ^{[1
]}

机构：

[1] School of Automation and Electrical Engineering, University of Science & Technology Beijing

来源：

JournalofSystemsEngineeringandElectronics | 2016年 / 27卷 / 06期

基金：

中国国家自然科学基金;

关键词：

p-norm; multiple kernel learning(MKL); manifold regularization; semi-supervised classification;

D O I：

暂无

中图分类号：

TP181 [自动推理、机器学习];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：1315 / 1325

页数：11

共 5 条

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