局部敏感非负矩阵分解

非负矩阵分解是一种新的基于部分学习的矩阵分解方法，反映了人类思维中局部构成整体的概念。算法只将非负矩阵近似地分解成两个非负矩阵的积，忽略了数据几何结构和判别信息。提出了一个局部敏感非负矩阵分解降维算法来克服这一缺点。该算法既保持了数据非负性，又保持了数据的几何结构和判别信息。构造了一个有效的乘积更新算法并且在理论上证明了算法的收敛性。ORL和Yale人脸数据库实验表明该算法性能超过许多已存在的方法。

Local Sensitive Nonnegative Matrix Factorization

Non-negative matrix factorization (NMF) is a new matrix decomposition method based on the part of the study,which has a reflection of human thinking partial constitute the overall concept. It only find two nonnegative matrices whose product can approximate the nonncgative data matrix without considering the geometric structure and the discriminative information in the data. We presented a local sensitive nonnegative matrix factorization for dimensionality to overcome the disadvantage, which preserves not only the nonnegativity but also the geometric structure and discriminative information of the data. An efficient multiplicative updating procedure was produced, and its convergence was gua-ranteed theoretically. Experiments on ORL and Yale face recognition databases demonstrate that proposed method outperforms many existing dimensionality reduction methods.