计算机科学与探索2017，Vol.11Issue(2)：303-313,11.DOI:10.3778/j.issn.1673-9418.1510018
流形与成对约束联合正则化半监督分类方法
Semi-Supervised Classification Method Based on Joint Regularization of Manifold and Pairwise Constraints
摘要
Abstract
In order to improve the learning performance,semi-supervised learning methods aim at exploiting the knowledge of a small amount of labeled examples as well as lots of unlabeled data instances simultaneously.However,most existing semi-supervised approaches,primarily focus on the effective utilization of those label-unknown data,and the successive study regarding the label-known examples is usually neglected.In light of such situation,in terms of the manifold regularization framework,this paper proposes a novel semi-supervised classification method based on joint regularization of manifold and pairwise constraints (SSC-JRMPC).This method proceeds from two aspects:on one hand,inheriting from the manifold regularization framework,the optimization regarding both empirical risk and structural risk,and the use of intrinsic data distribution of entire dataset are considered concurrently;on the other hand,by transforming the sample labels into the must-link/cannot-link pairwise constraint conditions and incorporating these extended knowledge into own objective formulation,the knowledge existing in the supervision information is further mined.As the results,the classification accuracy of SSC-JRMPC is distinctly enhanced.The experiments on realworld datasets confirm the merits of this paper work.关键词
半监督学习/分类/流形正则化/成对约束Key words
semi-supervised learning/classification/manifold regularization/pairwise constraints分类
信息技术与安全科学引用本文复制引用
奚臣,钱鹏江,顾晓清,蒋亦樟..流形与成对约束联合正则化半监督分类方法[J].计算机科学与探索,2017,11(2):303-313,11.基金项目
The National Natural Science Foundation of China under Grant No.61202311(国家自然科学基金) （国家自然科学基金）
the Natural Science Foundation of Jiangsu Province under Grant No.BK201221834(江苏省自然科学基金) （江苏省自然科学基金）
the R&D Frontier Program of Jiangsu Province under Grant No.BY2013015-02 (江苏省产学研前瞻性研究项目). （江苏省产学研前瞻性研究项目）
