A4 Article in conference proceedings
Group Nonnegative Matrix Factorization with Sparse Regularization in Multi-set Data (2020)

Wang, X., Liu, W., Cong, F., & Ristaniemi, T. (2020). Group Nonnegative Matrix Factorization with Sparse Regularization in Multi-set Data. In EUSIPCO 2020 : 28th European Signal Processing Conference (pp. 2125-2129). IEEE. European Signal Processing Conference. https://doi.org/10.23919/Eusipco47968.2020.9287756

JYU authors or editors

Publication details

All authors or editors: Wang, Xiulin; Liu, Wenya; Cong, Fengyu; Ristaniemi, Tapani

Parent publication: EUSIPCO 2020 : 28th European Signal Processing Conference

Place and date of conference: Amsterdam, Netherlands, 18.-21.1.2021

ISBN: 978-1-7281-5001-7

eISBN: 978-9-0827-9705-3

Journal or series: European Signal Processing Conference

ISSN: 2219-5491

eISSN: 2076-1465

Publication year: 2020

Pages range: 2125-2129

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.23919/Eusipco47968.2020.9287756

Publication open access: Not open

Publication channel open access:


Constrained joint analysis of data from multiplesources has received widespread attention for that it allowsus to explore potential connections and extract meaningful hidden components. In this paper, we formulate a flexible jointsource separation model termed as group nonnegative matrix factorization with sparse regularization (GNMF-SR), which aimsto jointly analyze the partially coupled multi-set data. In the GNMF-SR model, common and individual patterns of particular underlying factors can be extracted simultaneously with imposing nonnegative constraint and sparse penalty. Alternating optimization and alternating direction method of multipliers (ADMM) are combined to solve the GNMF-SR model. Using the experimentof simulated fMRI-like data, we demonstrate the ADMM-basedGNMF-SR algorithm can achieve the better performance.

Keywords: signal processing; machine learning; algorithms

Free keywords: Alternating direction method of multipliers; coupled; group nonnegative matrix factorization; joint analysis; sparse representation

Contributing organizations

Other organizations:

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1

Last updated on 2021-07-07 at 21:30