关键词: 核矩阵, 非负分解, 人脸图像, 特征提取

Abstract: A novel kernel-projection non-negative matrix factorization algorithm is proposed to improve the poor convergence of the traditional non-negative matrix factorization for the higher sample dimension in this study. The kernel matrix, characterizing the similarity of the samples, is achieved by the kernel projection to decrease samples’ within-class scatter and increase the between-class scatter, which can suppress the interior noise and improve linear separability of the samples. The kernel projection matrix is decomposed into basis vectors and weight coefficient matrix with the non-negative constraint. The weight coefficient matrix as the original samples characteristics is utilized for the image analysis. Face image feature extraction and classification experiments show that the proposed algorithm can extract the features better and improve the classification accuracy.

Key words: kernel projection matrix, non-negative factorization, face image, feature extraction