Faster Robust Tensor Power Method for Arbitrary Order
This work addresses a fundamental bottleneck in tensor decomposition for researchers and practitioners dealing with high-dimensional data, offering a more general and efficient solution.
The paper tackles the problem of decomposing arbitrary order tensors by introducing a novel tensor power method that overcomes limitations of existing approaches restricted to lower-order tensors or requiring strong assumptions, achieving a running time of O~(n^{p-1}) for a p-th order tensor.
Tensor decomposition is a fundamental method used in various areas to deal with high-dimensional data. \emph{Tensor power method} (TPM) is one of the widely-used techniques in the decomposition of tensors. This paper presents a novel tensor power method for decomposing arbitrary order tensors, which overcomes limitations of existing approaches that are often restricted to lower-order (less than $3$) tensors or require strong assumptions about the underlying data structure. We apply sketching method, and we are able to achieve the running time of $\widetilde{O}(n^{p-1})$, on the power $p$ and dimension $n$ tensor. We provide a detailed analysis for any $p$-th order tensor, which is never given in previous works.