Published in SoftwareX, 2023
Software paper for MatCoupLy – a Python package for fitting constrained coupled matrix factorisations and PARAFAC2 models.
Roald M, MatCoupLy: Learning coupled matrix factorizations with Python. SoftwareX. 2023;21:101292. https://www.sciencedirect.com/science/article/pii/S2352711022002102
Published in IEEE Transactions on Mobile Computing, 2022
Paper using the CANDECOMP/PARAFAC model to analyse variation in mobile network speed in real data with missing values.
Fida MR, Roald M, Acar E, Elmokashfi A. Modeling Variation in Mobile Download Speed in Presence of Missing Samples. IEEE Transactions on Mobile Computing. 2022. https://ieeexplore.ieee.org/document/9999262
Published in Journal of Open Source Software (JOSS), 2022
Software paper for TensorLy-Viz – a Python package for analysing and visualising tensor decomposition models.
Roald M, Moe YM. TLViz: Visualising and analysing tensor decomposition models with Python. Journal of Open Source Software. 2022;7(79):4754. https://joss.theoj.org/papers/10.21105/joss.04754
Published in SIAM Journal on Mathematics of Data Science (SIMODS), 2022
Journal paper of the AO-ADMM algorithm for fitting regularized PARAFAC2 models. Also includes some general theory on the PARAFAC2 constraints.
Roald M, Schenker C, Calhoun VD, Adali T, Bro R, Cohen JE, Acar E. An AO-ADMM approach to constraining PARAFAC2 on all modes. SIAM Journal on Mathematics of Data Science. 2022;4(3):1191-222. https://arxiv.org/abs/2110.01278
Published in Frontiers in neuroscience, 2022
In this paper, we compare the PARAFAC2 method and joint ICA and IVA for tracing evolving networks from fMRI data. It is a continuation of the work published at ICASSP'20
Acar E, Roald M, Hossain KM, Calhoun VD, Adali T. Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches. Frontiers in neuroscience. 2022;16. https://www.frontiersin.org/articles/10.3389/fnins.2022.861402/full
Published in EUSIPCO'21, 2021
Initial publication of the AO-ADMM algorithm for fitting regularized PARAFAC2 models.
Roald M, Schenker C, Cohen JE, Acar E. PARAFAC2 AO-ADMM: constraints in all modes. 2021 29th European Signal Processing Conference (EUSIPCO) 2021 Aug 23 (pp. 1040-1044). EURASIP. https://arxiv.org/abs/2102.02087
Published in ICASSP'20, 2020
This conference paper demonstrates that PARAFAC2 can successfully trace network evolution from fMRI images.
Roald M, Bhinge S, Jia C, Calhoun V, Adalı T, Acar E. Tracing network evolution using the parafac2 model. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 May 4 (pp. 1100-1104). IEEE. https://arxiv.org/abs/1911.02926