为了提高图像分类准确率，提出了一种基于低秩表示的非负张量分解算法。作为压缩感知理论的推广和发展，低秩表示将矩阵的秩作为一种稀疏测度，由于矩阵的秩反映了矩阵的固有特性，所以低秩表示能有效地分析和处理矩阵数据，把低秩表示引入到张量模型中，即引入到非负张量分解算法中，进一步扩展非负张量分解算法。实验结果表明，所提算法与其他相关算法相比，分类结果较好。%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.
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