Fairer non-negative matrix factorization.

Lara Kassab, Erin George, Deanna Needell, Haowen Geng, Nika Jafar Nia, Aoxi Li

Abstract read

In one paragraph

Article in Frontiers in big data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Lara KassabDepartment of Mathematics, California State University, Fullerton, Fullerton, CA, United States.

Erin GeorgeDepartment of Mathematics, University of California, San Diego, La Jolla, CA, United States.

Deanna NeedellDepartment of Mathematics, University of California, Los Angeles, Los Angeles, CA, United States.

Haowen GengMcCormick School of Engineering, Northwestern University, Evanston, IL, United States.

Nika Jafar NiaDepartment of Mathematics, Amherst College, Amherst, MA, United States.

Aoxi LiDepartment of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical and approachable to the practitioner. Motivated by recent work on "fair" PCA, here we consider the more challenging method of non-negative matrix factorization (NMF) as both a showcasing example and a method that is important in its own right for both topic modeling tasks and feature extraction for other ML tasks. We demonstrate that a modification of the objective function, by using a min-max formulation, may

Indexed as

dimensionality reductionFairer-NMFfairnessnon-negative matrix factorizationtopic modeling

Identifiers

PMID41924354

DOI10.3389/fdata.2026.1737043

PMCPMC13035732

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.