一个三阶张量的稀疏分解方法及其应用
A Sparse Factorization Strategy for Third-Order Tensors and Its Application
摘要: 本文主要研究张量的分解算法及其应用。在张量的T-SVD算法的基础上提出TST-SVD算法，并且解决了T-SVD算法在分解时可能无法保留其主要信息的情况。而我们的TST-SVD则通过软阈值的方法可以很好地保留其主要信息，并且对于噪音图片也可以达到很好去噪效果。在图片的识别和重构方面也有着不错的表现。
Abstract: This paper mainly studies the decomposition algorithm of tensor and its application. TST-SVD al-gorithm is proposed on the basis of T-SVD algorithm of tensor, and it solves the case that the T-SVD algorithm may not be able to retain its main information when decomposing. However, our TST-SVD can keep its main information well through the soft threshold method, and achieve a good denoising effect for noise images. It also has a good performance in image recognition and reconstruction.
文章引用：汪亮. 一个三阶张量的稀疏分解方法及其应用[J]. 应用数学进展, 2018, 7(8): 1119-1126. https://doi.org/10.12677/AAM.2018.78129

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