摘要
带稀疏约束的优化模型常用于主成分分析和压缩感知等领域.随着张量研究的推进,高阶主成分分析和高阶压缩感知也被提出并取得一些研究成果.本文提出一个带稀疏约束的张量Z-特征向量求解的数学问题,并设计算法进行求解.
Sparse constrained optimization models are commonly used in the fields of principal component analysis and compressed sensing. With the advance of tensor research, higher-order principal component analysis and higher-order compressed sensing have also been proposed and achieved some research results. In this paper, we design an algorithm to solve tensor Z-eigenvectors with sparse constraints.
引文
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