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
Publication date: 24/01/2021
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:
Abstract
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
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1
