计算机应用研究Issue(1)：300-303,4.DOI:10.3969/j.issn.1001-3695.2016.01.069
基于低秩表示的非负张量分解算法
Non-negative tensor factorization algorithm based on low rank representation
摘要
Abstract
This paper proposed a non-negative tensor decomposition algorithm based on low-rank representation to improve the accuracy of image classification.As the extension and the development of compressed sensing theory,the low-rank representa-tion denoted that the rank of the matrix could be used as a measurement of sparsity.Since the rank of a matrix reflected the in-herent property of the matrix,the low-rank analysis could effectively analyze and process the matrix data.This paper intro-duced the low-rank representation into tensor model,namely to introduce it into non-negative tensor decomposition algorithm and to further expand the non-negative tensor decomposition algorithm.Experimental results show that the classification accu-racy of the algorithms proposed in this paper is better compared to other existing algorithms.关键词
图像分类/低秩表示/非负/张量分解Key words
image classification/low rank representation/non-negative/tensor decomposition分类
信息技术与安全科学引用本文复制引用
刘亚楠,刘路路,罗斌..基于低秩表示的非负张量分解算法[J].计算机应用研究,2016,(1):300-303,4.基金项目
高校省级优秀青年人才基金重点项目（2011SQRL129ZD）；安徽省高校自然科学研究重点项目 （）
