2 citations · 4 across the 5 of their papers we have counts for
12 papers
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…
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),…