Image Feature Extraction via Graph Embedding Regularized Projective Non-negative Matrix Factorization

摘要：

　　Non-negative matrix factorization (NMF) has been widely used in image processing and pattern recognition fields.Unfortunately,NMF does not consider the geometrical structure and the discriminative information of data,which might make it unsuitable for classification tasks.In addition,NMF only calculates the coefficient matrix of the training data and how to yields the coefficient vector of a new test data is still obscure.In this paper,we propose a novel graph embedding regularized projective non-negative matrix factorization (GEPNMF) method to address the aforementioned problems.By introducing a graph embedding regularization term,the learned subspace can preserve the local geometrical structure of data while maximizing the margins of different classes.We deduce a multiplicative update rule (MUR) to iteratively solve the objective function of GEPNMF and prove its convergence in theory.Experimental results on ORL and CMU PIE databases suggest the effectiveness of GEPNMF.

关键词： Graph embedding Non-negative matrix factorization Feature extraction Face recognition

作者: Haishun Du Qingpu Hu Xudong Zhang Yandong Hou

作者单位: Institute of Image Processing and Pattern Recognition,Henan University,Kaifeng 475004,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 196-209

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）