activity
20192022
most citedSemi-supervised Nonnegative Matrix Factorization for Document Classification

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.IR20221 cited

Semi-supervised Nonnegative Matrix Factorization for Document Classification

Jamie Haddock, Lara Kassab, Sixian Li +9

We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators.…

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.DL2020

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.…

math.NA2019

Stochastic Iterative Hard Thresholding for Low-Tucker-Rank Tensor Recovery

Rachel Grotheer, Shuang Li, Anna Ma +2

Low-rank tensor recovery problems have been widely studied in many applications of signal processing and machine learning. Tucker decomposition is known as one of the most popular…

math.NA2019

Iterative Hard Thresholding for Low CP-rank Tensor Models

Rachel Grotheer, Shuang Li, Anna Ma +2

Recovery of low-rank matrices from a small number of linear measurements is now well-known to be possible under various model assumptions on the measurements. Such results demonstr…