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20162022
most citedCOVID-19 Time-series Prediction by Joint Dictionary Learning and Online NMF

3 citations · 10 across the 20 of their papers we have counts for

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12 papers · 1 filter

cs.LG20212 cited

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…

cs.LG2021

Analysis of Legal Documents via Non-negative Matrix Factorization Methods

Ryan Budahazy, Lu Cheng, Yihuan Huang +7

The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assist…

cs.LG2020

Applications of Online Nonnegative Matrix Factorization to Image and Time-Series Data

Hanbaek Lyu, Georg Menz, Deanna Needell +1

Online nonnegative matrix factorization (ONMF) is a matrix factorization technique in the online setting where data are acquired in a streaming fashion and the matrix factors are u…

cs.LG2020

Semi-supervised NMF Models for Topic Modeling in Learning Tasks

Jamie Haddock, Lara Kassab, Sixian Li +9

We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific d…

cs.LG2020

On a Guided Nonnegative Matrix Factorization

Joshua Vendrow, Jamie Haddock, Elizaveta Rebrova +1

Fully unsupervised topic models have found fantastic success in document clustering and classification. However, these models often suffer from the tendency to learn less-than-mean…

cs.LG20203 cited

COVID-19 Time-series Prediction by Joint Dictionary Learning and Online NMF

Hanbaek Lyu, Christopher Strohmeier, Georg Menz +1

Predicting the spread and containment of COVID-19 is a challenge of utmost importance that the broader scientific community is currently facing. One of the main sources of difficul…