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20162024
most citedCOVID-19 Time-series Prediction by Joint Dictionary Learning and Online NMF

3 citations · 11 across the 23 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

math.NA20221 cited

Multi-Randomized Kaczmarz for Latent Class Regression

Erin George, Yotam Yaniv, Deanna Needell

Linear regression is effective at identifying interpretable trends in a data set, but averages out potentially different effects on subgroups within data. We propose an iterative a…

physics.soc-ph2022

Inference of Media Bias and Content Quality Using Natural-Language Processing

Zehan Chao, Denali Molitor, Deanna Needell +1

Media bias can significantly impact the formation and development of opinions and sentiments in a population. It is thus important to study the emergence and development of partisa…

stat.CO2022

Multi-scale Hybridized Topic Modeling: A Pipeline for Analyzing Unstructured Text Datasets via Topic Modeling

Keyi Cheng, Stefan Inzer, Adrian Leung +6

We propose a multi-scale hybridized topic modeling method to find hidden topics from transcribed interviews more accurately and efficiently than traditional topic modeling methods.…

math.NA2022

Online Signal Recovery via Heavy Ball Kaczmarz

Benjamin Jarman, Yotam Yaniv, Deanna Needell

Recovering a signal from a sequence of linear measurements is an important problem in areas such as computerized tomography and compressed sensing. In thi…

stat.CO2022

Population-Based Hierarchical Non-negative Matrix Factorization for Survey Data

Xiaofu Ding, Xinyu Dong, Olivia McGough +6

Motivated by the problem of identifying potential hierarchical population structure on modern survey data containing a wide range of complex data types, we introduce population-bas…

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.…