电子学报Issue(11)：2198-2204,7.DOI:10.3969/j.issn.0372-2112.2014.11.011
稀疏特征空间嵌入正则化：鲁棒的半监督学习框架
Sparse Feature Space Embedding Regularization:A Framework of Robust Semi-Supervised Learning
摘要
Abstract
Semi-supervised learning(SSL) ,as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data ,has been attracting increasing attention in machine learning community .Of various SSL methods ,graph based approaches have attracted more extensive research due to their elegant mathematical formulation and good performance .How-ever ,there may exist several nontrivial concerns such as such as model parameters sensitiveness and insufficient discriminative infor-mation in data space ,etc ,in existing graph based SSL approaches .To these ends ,in this paper ,we propose a robust Sparse Feature Space embedding Regularization (SFSR )SSL framework .The main idea of the proposed SFSR includes three folds:(1 )linearly em-bedding input data into its feature spaces (2 )sparsely reconstructing input data using its feature space embedding projection images;and (3 )preserving the same sparse representation relationship among labels of data as that among data in some label space spanned linearly by input data ,thus constructing a novel sparse nearest feature space embedding regularizer ,coined as SFSR .The comprehen-sive experimental results on several real-world benchmark databases are presented to demonstrate the significantly robust effective-ness of our proposed method .关键词
基于图的半监督学习/稀疏表示/最近特征空间嵌入/正则化Key words
graph based semi-supervised learning/sparse representation/nearest feature space embedding/regularization分类
信息技术与安全科学引用本文复制引用
陶剑文,姚奇富..稀疏特征空间嵌入正则化：鲁棒的半监督学习框架[J].电子学报,2014,(11):2198-2204,7.基金项目
教育部人文社会科学研究规划基金（No ．13YJAZH084）；浙江省自然科学基金（No ．LY14F02009）；宁波市自然科学基金 （）
