Singular value decompositions and low rank approximations of tensors
IEEE Transactions on Signal Processing
Product form approximations for communicating Markov processes
Performance Evaluation
A New Truncation Strategy for the Higher-Order Singular Value Decomposition
SIAM Journal on Scientific Computing
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It is known that a higher order tensor does not necessarily have an optimal low rank approximation, and that a tensor might not be orthogonally decomposable (i.e., admit a tensor SVD). We provide several sufficient conditions which lead to the failure of the tensor SVD, and characterize the existence of the tensor SVD with respect to the higher order SVD (HOSVD). In the face of these difficulties to generalize standard results known in the matrix case to tensors, we consider the low rank orthogonal approximation of tensors. The existence of an optimal approximation is theoretically guaranteed under certain conditions, and this optimal approximation yields a tensor decomposition where the diagonal of the core is maximized. We present an algorithm to compute this approximation and analyze its convergence behavior. Numerical experiments indicate a linear convergence rate for this algorithm.