计算机工程与应用2012，Vol.48Issue(36)：194-200,7.DOI:10.3778/j.issn.1002-8331.1205-0357
基于图正则化的半监督非负矩阵分解
Graph regularized-based semi-supervised non-negative matrix factorization
摘要
Abstract
This paper presents a novel algorithm called Graph regularized-based Semi-supervised NMF(GSNMF). It overcomes the shortcomings which ignore the geometric structure and the label information of the data for Non-negative Matrix Factorization(NMF), Constrained NMF(CNMF) and Graphed regularized NMF(GNMF). Moreover, those algorithms are special case of GSNMF. The convergence proof of this algorithm is provided. GSNMF preserves the intrinsic geometry of data and uses the label information as semi-supervised learning. It makes nearby samples with the same class-label more compact, and nearby classes separated. Compared with NMF, LNMF, PNMF, GNMF and CNMF, experiment results on ORL face database, FERET face database and USPS handwrite database have shown that the proposed method achieves better clustering results.关键词
图像聚类/半监督学习/非负矩阵分解/图正则化Key words
image clustering/ semi-supervised learning/ Non-negative Matrix Factorization(NMF)/ graph regularized分类
信息技术与安全科学引用本文复制引用
杜世强,石玉清,王维兰,马明..基于图正则化的半监督非负矩阵分解[J].计算机工程与应用,2012,48(36):194-200,7.基金项目
国家自然科学基金(No.61162021) （No.61162021）
西北民族大学中青年科研基金(No.12xb30) （No.12xb30）
西北民族大学科研创新团队计划. （）
