计算机工程与应用2017，Vol.53Issue(8)：61-67,7.DOI:10.3778/j.issn.1002-8331.1510-0153
保邻域结构的拉普拉斯特征映射延拓
Out-of-sample extension of Laplacian Eigenmaps preserving local structure
摘要
Abstract
To solve out-of-sample problem of Laplacian Eigenmaps, a method preserving local structure is proposed which is based on the assumption that there is a linear relationship between the new sample and its neighbors. Then sparse-coding is used to obtain the linear reconstruction coefficients between the new sample and its neighbors. Finally, the low-dimensional representation of the new sample is computed through the linear relationship. The classification of the low-dimensional representation is made by 1-NN classifier. Compared with sparse-coding reconstruction method based on global relationship, the method based on local information achieves higher accuracy using less time showing its superiority. Furthermore, the proposed method can be easily extended to the out-of-sample problem of other non-linear dimensionality reduction methods.关键词
新增样本点延拓/稀疏编码/局部结构Key words
out-of-sample extension/sparse-coding/local structure分类
信息技术与安全科学引用本文复制引用
王伟文,方环,张传林..保邻域结构的拉普拉斯特征映射延拓[J].计算机工程与应用,2017,53(8):61-67,7.基金项目
国家自然科学基金(No.61070165) （No.61070165）
广东省教育部产学研结合项目(No.2011B090400458). （No.2011B090400458）
