64 citations · 179 across the 24 of their papers we have counts for
9 papers · 1 filter
Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruption
HanQin Cai, Zehan Chao, Longxiu Huang +1
We study the tensor robust principal component analysis (TRPCA) problem, a tensorial extension of matrix robust principal component analysis (RPCA), that aims to split the given te…
Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions
HanQin Cai, Keaton Hamm, Longxiu Huang +1
Low rank tensor approximation is a fundamental tool in modern machine learning and data science. In this paper, we study the characterization, perturbation analysis, and an efficie…
Tensor Completion through Total Variationwith Initialization from Weighted HOSVD
Zehan Chao, Longxiu Huang, Deanna Needell
In our paper, we have studied the tensor completion problem when the sampling pattern is deterministic. We first propose a simple but efficient weighted HOSVD algorithm for recover…
HOSVD-Based Algorithm for Weighted Tensor Completion
Zehan Chao, Longxiu Huang, Deanna Needell
Matrix completion, the problem of completing missing entries in a data matrix with low dimensional structure (such as rank), has seen many fruitful approaches and analyses. Tensor…
Stability of Sampling for CUR Decompositions
Keaton Hamm, Longxiu Huang
This article studies how to form CUR decompositions of low-rank matrices via primarily random sampling, though deterministic methods due to previous works are illustrated as well.…
Perturbations of CUR Decompositions
Keaton Hamm, Longxiu Huang
The CUR decomposition is a factorization of a low-rank matrix obtained by selecting certain column and row submatrices of it. We perform a thorough investigation of what happens to…
Perspectives on CUR Decompositions
Keaton Hamm, Longxiu Huang
This note discusses an interesting matrix factorization called the CUR Decomposition. We illustrate various viewpoints of this method by comparing and contrasting them in different…
CUR Decompositions, Approximations, and Perturbations
Keaton Hamm, Longxiu Huang
This article discusses a useful tool in dimensionality reduction and low-rank matrix approximation called the CUR decomposition. Various viewpoints of this method in the literature…
Dynamical sampling with additive random noise
Akram Aldroubi, Longxiu Huang, Ilya Krishtal +3
Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamic…