In this paper, we study the characterization, perturbation analysis, and an efficient sampling strategy for two primary tensor CUR approximations, namely ...
Mar 19, 2021 · In this paper, we study the characterization, perturbation analysis, and an efficient sampling strategy for two primary tensor CUR approximations.
In this work, we generalize CUR decompositions to high-order tensors under the low-multilinear-rank setting. We provide two verisons of this generalization, ...
Jan 1, 2021 · In this paper, we study the characterization, perturbation analysis, and an efficient sampling strategy for two primary tensor CUR ...
Apr 2, 2021 · In this paper, we study the characterization, perturbation analysis, and an efficient sampling strategy for two primary tensor CUR ...
Oct 22, 2024 · RTCUR uses an alternating projection framework and employs a novel mode-wise tensor decomposition [22] for fast low-rank tensor approximation.
This article discusses a useful tool in dimensionality reduction and low-rank matrix approximation called the CUR decomposition.
Hamm, L.-X. Huang, and Deanna Needell Mode-wise Tensor Decompositions: Multidimensional Generalizations of CUR Decompositions Journal of Machine Learning ...
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Mode-wise tensor decompositions: multi-dimensional generalizations of CUR decompositions. Low rank tensor approximation is a fundamental tool in modern machine ...
May 1, 2023 · The purpose of this note is to characterize various types of pseudoskeleton decompositions for matrices and tensors over arbitrary fields.