基于特征再分解的数据稀疏表示

摘要

The basic features learned by nonnegative matrix factorization ( NMF ) are usually smooth because the objective function is non-convex for both nonnegative factors simultaneously. To enhance the sparseness of the basic features for NMF, feature re-factorization-based data sparse representation is proposed by looking into the essential similarity between original data and basic features. A set of rough features are obtained from parts of data by making use of several prior regularizations, which are some semantic face images. Applying NMF on these semantic faces, the latent features are further outstanding by holding salient features and eliminating overlapping between rough features. The results from synthetic swimmer dataset and face datasets show that the final latent features are sparser and more powerful for sparse representation than NMF.%针对因非负矩阵分解模型目标函数非凸而出现局部次优基特征平滑的现象,提出基于特征再分解的数据稀疏表示方法,在多种先验正则信息约束下初步挖掘原始数据的潜在特征,再秉承非负加性线性表示方式的"局部构成整体"的认知优势,利用非负矩阵分解对特征突显的信息再次凝练,获取数据潜在本征信息,实现非负数据稀疏表示.算法在合成的Swimmer和人脸图像数据的实验结果表明,与传统非负矩阵分解方法相比,该方法的基特征稀疏性得到增强,且判别能力也获得显著提高.