一种基于块Krylov迭代的三阶张量低管秩逼近的近似算法
An Approximation Algorithm for Third-order Tensor Low-tubal Rank Approximation Based on Block Krylov Iteration
薛睿琪 1凌晨1
作者信息
- 1. 杭州电子科技大学理学院,浙江 杭州 310018
- 折叠
摘要
基于块Krylov迭代和随机计数嵌入矩阵技术,提出了一种新的张量低管秩逼近算法.利用块Krylov迭代和随机计数嵌入矩阵技术,保证了算法精度处于较高水平.与其他几种常见算法相比,彩色图片实验结果表明,所提出的算法具有峰值性噪比值高而运算时间短的特点;人工合成数据实验表明,提出的算法在投影误差和相对误差上同时占优.
Abstract
A new tensor low-tubal rank approximation algorithm is proposed,which is based on block Krylov iteration and random count embedding matrix techniques.By using block Krylov iteration and random count embedding matrix techniques,the accuracy of the algorithm is guaranteed at a high level.Compared with other prevailing algorithms,the experimental results of color images show that the proposed algorithm has higher PSNR values and less computing time.The synthetic data experiments show that the proposed algorithm is superior to prevailing methods in terms of projection error and relative error.
关键词
三阶张量/张量奇异值分解/管秩/块Krylov迭代/随机计数嵌入Key words
third-order tensor/tensor singular value decomposition/tubal rank/block Krylov iteration/random count embedding引用本文复制引用
出版年
2024
NETL
NSTL
万方数据