Nonnegative matrix factorization with bounded total variational regularization for face recognition

作者：Yin, Haiqing^*; Liu, Hongwei

来源：Pattern Recognition Letters, 2010, 31(16): 2468-2473.

DOI：10.1016/j.patrec.2010.08.001

• Nonnegative matrix factorization
• Total variation regularization
• Face recognition

Nonnegative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of nonnegative data based on minimizing least square error (L-2 norm). However it has been observed that the proper norm for images is the bounded total variation (TV) norm other than the L-2 norm. The space of functions of bounded TV allows discontinuous solution and plays an important role in image processing. In this paper, we propose a new NMF model with bounded TV regularization for identifying discriminate representation of image patterns. We provide a simple update rule for computing the factorization and give supporting theoretical analysis. Finally, we perform a series of numerical experiments to show evidence of the good behavior of the numerical scheme.

• 出版日期2010-12-1
• 单位西安电子科技大学