2 citations · 3 across the 2 of their papers we have counts for
6 papers
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…
Estimation of high-dimensional change-points under a group sparsity structure
Hanqing Cai, Tengyao Wang
Change-points are a routine feature of 'big data' observed in the form of high-dimensional data streams. In many such data streams, the component series possess group structures an…
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…
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization
HanQin Cai, Yuchen Lou, Daniel McKenzie +1
We consider the zeroth-order optimization problem in the huge-scale setting, where the dimension of the problem is so large that performing even basic vector operations on the deci…
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),…
Accelerated Structured Alternating Projections for Robust Spectrally Sparse Signal Recovery
HanQin Cai, Jian-Feng Cai, Tianming Wang +1
Consider a spectrally sparse signal that consists of complex sinusoids with or without damping. We study the robust recovery problem for the spectrally sparse…