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Faster Robust Tensor Power Method for Arbitrary Order

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arxiv 2306.00406 v1 pith:SGXLBNWJ submitted 2023-06-01 cs.LG cs.NAmath.NA

classification cs.LGcs.NAmath.NA
keywords tensormethodpowerordertensorsarbitrarydatadecomposition
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abstract

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.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse

    cs.LG 2025-05 reject novelty 4.0 of 10

    A residual self-attention network with all weight entries bounded by a small η can be approximated by one layer to error O(η)‖X‖∞, so skip connections do not prevent layer collapse.

  2. Universal Approximation of Visual Autoregressive Transformers

    cs.LG 2025-02 reject novelty 4.0 of 10

    The paper's headline claim that VAR transformers universally approximate all Lipschitz image maps is not supported, because the theorem restricts the target class and its key lemma has an invalid linearity step.

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