首页> 外文期刊>SIAM Journal on Matrix Analysis and Applications >CANONICAL POLYADIC DECOMPOSITION OF THIRD-ORDER TENSORS: REDUCTION TO GENERALIZED EIGENVALUE DECOMPOSITION?
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CANONICAL POLYADIC DECOMPOSITION OF THIRD-ORDER TENSORS: REDUCTION TO GENERALIZED EIGENVALUE DECOMPOSITION?

机译:三阶张量的规范多角度分解:归一化的特征值分解?

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Canonical polyadic decomposition (CPD) of a third-order tensor is decomposition in a minimal number of rank-1 tensors. We call an algorithm algebraic if it is guaranteed to find the decomposition when it is exact and if it relies only on standard linear algebra (essentially sets of linear equations and matrix factorizations). The known algebraic algorithms for the computation of the CPD are limited to cases where at least one of the factor matrices has full column rank. In this paper we present an algebraic algorithm for the computation of the CPD in cases where none of the factor matrices has full column rank. In particular, we show that if the famous Kruskal condition holds, then the CPD can be found algebraically.
机译:三阶张量的规范多态分解(CPD)是在最少数量的1级张量中进行的分解。如果可以保证精确找到分解并且仅依赖于标准线性代数(本质上是线性方程组和矩阵分解),则我们将其称为算法代数。用于计算CPD的已知代数算法仅限于至少一个因子矩阵具有完整列秩的情况。在本文中,我们提出了一种在没有任何因子矩阵具有完整列秩的情况下计算CPD的代数算法。特别地,我们表明,如果著名的Kruskal条件成立,那么CPD可以代数找到。

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