福州大学学报（自然科学版）2011，Vol.39Issue(1)：49-53,5.DOI:CNKI:35-1117/N.20110121.1724.011
基于监督学习的核拉普拉斯特征映射分类方法
Classification based on supervised kernel Laplacian eigienmaps
摘要
Abstract
Proposes a method named supervised kernel Laplacian eigenmaps (SKIE), which suggests using the kernel non - linear mapping to project the sample data onto the high - dimensional kernel characteristic space, and then combining the samples of manifold architecture and category information effectively, and finally extracting the low -dimensional manifolds features embedded in high -dimensional data for classification. Experiments show that the method has a generalization performance to new samples, and can effectively improve the classification performance.关键词
监督学习/拉普拉斯特征映射/流形学习/核方法Key words
manifold learning/ Laplacian eigenmaps/ supervised/ kernel method分类
信息技术与安全科学引用本文复制引用
张建波,朱敏琛..基于监督学习的核拉普拉斯特征映射分类方法[J].福州大学学报（自然科学版）,2011,39(1):49-53,5.基金项目
福建省自然科学基金资助项目(2009J01283、2009J01248) （2009J01283、2009J01248）
福建省科技计划重点资助项目(2008H0026) （2008H0026）
