64 citations · 179 across the 24 of their papers we have counts for
28 papers
Quantized Low-Rank Quantum State Tomography: Hyperbolic Quantization and Riemannian Least-Squares Recovery
HanQin Cai, Longxiu Huang, Juntao You
We study low-rank quantum state tomography from finite-bit Pauli batch responses. To avoid bias introduced by generic quantization, we propose HyperQuant, a mean-preserving hyperbo…
Robust Low-Tubal-Rank Tensor Completion under Cross-Concentrated Sampling
HanQin Cai, Longxiu Huang, Jing Qin +1
Tensor cross-concentrated sampling (t-CCS) bridges entrywise sampling and t-CUR slice-wise sampling by observing entries only within selected horizontal and lateral slices. Existin…
Property Inheritance for Subtensors in Tensor Train Decompositions
HanQin Cai, Longxiu Huang
Tensor dimensionality reduction is one of the fundamental tools for modern data science. To address the high computational overhead, fiber-wise sampled subtensors that preserve the…
Three-dimensional signal processing: a new approach in dynamical sampling via tensor products
Yisen Wang, Hanqin Cai, Longxiu Huang
The dynamical sampling problem is centered around reconstructing signals that evolve over time according to a dynamical process, from spatial-temporal samples that may be noisy. Th…
Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion
Bowen Su, Juntao You, HanQin Cai +1
While Bernoulli sampling is extensively studied in tensor completion, t-CUR sampling approximates low-tubal-rank tensors via lateral and horizontal subtensors. However, both method…
Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descent
HanQin Cai, Longxiu Huang, Xiliang Lu +1
This paper studies the robust Hankel recovery problem, which simultaneously removes the sparse outliers and fulfills missing entries from the partial observation. We propose a nove…
On the Robustness of Cross-Concentrated Sampling for Matrix Completion
HanQin Cai, Longxiu Huang, Chandra Kundu +1
Matrix completion is one of the crucial tools in modern data science research. Recently, a novel sampling model for matrix completion coined cross-concentrated sampling (CCS) has c…
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…
Non-convex approaches for low-rank tensor completion under tubal sampling
Zheng Tan, Longxiu Huang, HanQin Cai +1
Tensor completion is an important problem in modern data analysis. In this work, we investigate a specific sampling strategy, referred to as tubal sampling. We propose two novel no…
Randomized Kaczmarz in Adversarial Distributed Setting
Longxiu Huang, Xia Li, Deanna Needell
Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world…
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
HanQin Cai, Longxiu Huang, Pengyu Li +1
While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples. Unfortunately, both samp…
Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents
Pengyu Li, Christine Tseng, Yaxuan Zheng +4
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as label…
Robust recovery of bandlimited graph signals via randomized dynamical sampling
Longxiu Huang, Deanna Needell, Sui Tang
Heat diffusion processes have found wide applications in modelling dynamical systems over graphs. In this paper, we consider the recovery of a -bandlimited graph signal that is…
Predictive algorithms in dynamical sampling for burst-like forcing terms
Akram Aldroubi, Longxiu Huang, Keri Kornelson +1
In this paper, we consider the problem of recovery of a burst-like forcing term in an initial value problem (IVP) in the framework of dynamical sampling. We introduce an idea of us…
Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition
HanQin Cai, Zehan Chao, Longxiu Huang +1
We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their…
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…
Robust CUR Decomposition: Theory and Imaging Applications
HanQin Cai, Keaton Hamm, Longxiu Huang +1
This paper considers the use of Robust PCA in a CUR decomposition framework and applications thereof. Our main algorithms produce a robust version of column-row factorizations of m…
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation
HanQin Cai, Keaton Hamm, Longxiu Huang +2
Robust principal component analysis (RPCA) is a widely used tool for dimension reduction. In this work, we propose a novel non-convex algorithm, coined Iterated Robust CUR (IRCUR),…
COVID-19 Literature Topic-Based Search via Hierarchical NMF
Rachel Grotheer, Yihuan Huang, Pengyu Li +7
A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available.…
Sampling the flow of a bandlimited function
Akram Aldroubi, Karlheinz Gröchenig, Longxiu Huang +3
We analyze the problem of reconstruction of a bandlimited function from the space-time samples of its states resulting from the convolution with a kernel .…
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…