论文标题
基于非负/二进制基质分解的图像分析
Image Analysis Based on Nonnegative/Binary Matrix Factorization
论文作者
论文摘要
使用非负/二进制矩阵分解（NBMF），可以将矩阵分解为非负矩阵和二进制矩阵。我们基于NBMF和使用Fujitsu Digital Exealer的面部图像的分析导致了成功的图像重建和图像分类。 NBMF算法在迭代次数少于非负矩阵分解（NMF）所需的迭代率较少，尽管这两种技术在图像分类中的表现相当。
Using nonnegative/binary matrix factorization (NBMF), a matrix can be decomposed into a nonnegative matrix and a binary matrix. Our analysis of facial images, based on NBMF and using the Fujitsu Digital Annealer, leads to successful image reconstruction and image classification. The NBMF algorithm converges in fewer iterations than those required for the convergence of nonnegative matrix factorization (NMF), although both techniques perform comparably in image classification.
