  Low-rank tensor approximation error bounds are proposed for the case of noisy
input data that depend on low-rank representation type, rank and the
dimensionality of the tensor. The bounds show that high-dimensional low-rank
structured approximations provide superior noise-filtering properties compared
to matrices with the same rank and total element count.
