半监督凸非负矩阵分解

Semi-supervised convex nonnegative matrix factorization

基于流形正则化思想,提出了半监督凸非负矩阵分解算法.该算法通过类间图和类内图刻画数据的内在几何结构,使得所提算法不但具有数据矩阵凸分解特性,而且保持它的几何结构和判别信息.最后,人脸数据集上的实验研究表明所提算法能够获得良好的识别性能.

Based on manifold regularization,we develop a novel algorithm called Semi-supervised Convex Nonnegative Matrix Factorization（SCNMF）.SCNMF can capture the data intrinsic geometric structure with within-class graph and between-class graph.Not only does the proposed algorithm holds data matrix convex factorization,but also preserves geometric structure and discriminant analysis.Finally,experimental results demonstrate that it achieves encouraging results on face data sets.