基于低秩表示的非负张量分解算法

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的分析和处理矩阵数据，本文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，本文所提算法与其他相关算法相比，分类结果较好。

Non-negative Tensor Factorization Based on Low Rank Representation

This paper has proposed non-negative tensor decomposition 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 representation denotes that the rank of the matrix can be used as a measurement of sparsity. Since the rank of a matrix reflects the inherent property of the matrix, the low-rank analysis can effectively analyze and process the matrix data. This paper introduces 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 accuracy of the algorithms proposed in this paper is better compared to other existing algorithms.