Orthonormal projective non-negative matrix factorization (opNMF) has been widely used in neuroimaging and clinical neuroscience applications to derive representations of the brain in health and disease. The non-negativity and orthonormality constraints of opNMF result in intuitive and well-localized factors. However, the advantages of opNMF come at a steep computational cost that prohibits its use in large-scale data. In this work, we propose novel and scalable optimization schemes for orthonormal projective non-negative matrix factorization that enable the use of the method in large-scale neuroimaging settings. We replace the high-dimensional data matrix with its corresponding singular value decomposition (SVD) and QR decompositions and combine the decompositions with opNMF multiplicative update algorithm. Empirical validation of the proposed methods demonstrated significant speed-up in computation time while keeping memory consumption low without compromising the accuracy of the solution.

Original languageEnglish
Title of host publicationMedical Imaging 2023
Subtitle of host publicationImage Processing
EditorsOlivier Colliot, Ivana Isgum
ISBN (Electronic)9781510660335
StatePublished - 2023
EventMedical Imaging 2023: Image Processing - San Diego, United States
Duration: Feb 19 2023Feb 23 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2023: Image Processing
Country/TerritoryUnited States
CitySan Diego


Dive into the research topics of 'Scalable NMF via linearly optimized data compression'. Together they form a unique fingerprint.

Cite this