Blar i ODA Open Digital Archive på forfatter "Roald, Marie"

Viser treff 1-5 av 5

    • An AO-ADMM Approach to Constraining PARAFAC2 on All Modes 

      Roald, Marie; Schenker, Carla; Calhoun, Vince D.; Adali, Tülay; Bro, Rasmus; Cohen, Jeremy E.; Acar, Evrim (SIAM Journal on Mathematics of Data Science;Volume 4, Issue 3 | 2022, Peer reviewed; Journal article, 2022-08-30)
      Analyzing multi-way measurements with variations across one mode of the dataset is a challenge in various fields including data mining, neuroscience and chemometrics. For example, measurements may evolve over time or have ...

    • MatCoupLy: Learning coupled matrix factorizations with Python 

      Roald, Marie (SoftwareX;Volume 21, February 2023, 101292, Peer reviewed; Journal article, 2023-01-04)
      Coupled matrix factorization (CMF) models jointly decompose a collection of matrices with one shared mode. For interpretable decompositions, constraints are often needed, and variations of constrained CMF models have been ...

    • PARAFAC2 AO-ADMM: Constraints in all modes 

      Roald, Marie; Schenker, Carla; Cohen, Jeremy E.; Acar, Evrim (European Signal Processing Conference;2021 29th European Signal Processing Conference (EUSIPCO), Conference object, 2021-12-08)
      The PARAFAC2 model provides a flexible alternative to the popular CANDECOMP/PARAFAC (CP) model for tensor decompositions. Unlike CP, PARAFAC2 allows factor matrices in one mode (i.e., evolving mode) to change across tensor ...

    • TLViz: Visualising and analysing tensor decomposition models with Python 

      Roald, Marie; Moe, Yngve Mardal (Journal of Open Source Software (JOSS);, Peer reviewed; Journal article, 2022)
      Multi-way data, also known as tensor data or data cubes, occur in many applications, such as text mining (Bader et al., 2008), neuroscience (Andersen & Rayens, 2004) and chemical analysis (Bro, 1997). Uncovering the ...

    • Tracing Network Evolution Using The PARAFAC2 Model 

      Roald, Marie; Bhinge, Suchita; Jia, Chunying; Calhoun, Vince; Adalı, Tülay; Acar, Evrim (Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing;ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Peer reviewed; Journal article, 2020-04-09)
      Characterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatial networks of functional connectivity in ...