no code implementations • 14 Aug 2023 • Christos Chatzis, Max Pfeffer, Pedro Lind, Evrim Acar
Time-evolving data sets can often be arranged as a higher-order tensor with one of the modes being the time mode.
1 code implementation • 24 Oct 2022 • Carla Schenker, XiuLin Wang, Evrim Acar
Coupled matrix and tensor factorizations (CMTF) have emerged as an effective data fusion tool to jointly analyze data sets in the form of matrices and higher-order tensors.
no code implementations • 1 Sep 2022 • Florian Becker, Age K. Smilde, Evrim Acar
Low-rank data approximation methods such as matrix (e. g., non-negative matrix factorization) and tensor decompositions (e. g., CANDECOMP/PARAFAC) have demonstrated that they can provide such transparent and interpretable insights.
1 code implementation • 4 Oct 2021 • Marie Roald, Carla Schenker, Vince D. Calhoun, Tülay Adalı, Rasmus Bro, Jeremy E. Cohen, Evrim Acar
We also apply our model to two real-world datasets from neuroscience and chemometrics, and show that constraining the evolving mode improves the interpretability of the extracted patterns.
2 code implementations • 3 Feb 2021 • Marie Roald, Carla Schenker, Jeremy E. Cohen, Evrim Acar
The PARAFAC2 model provides a flexible alternative to the popular CANDECOMP/PARAFAC (CP) model for tensor decompositions.
2 code implementations • 19 Jul 2020 • Carla Schenker, Jeremy E. Cohen, Evrim Acar
Coupled matrix and tensor factorizations (CMTF) are frequently used to jointly analyze data from multiple sources, also called data fusion.
1 code implementation • 23 Oct 2019 • Marie Roald, Suchita Bhinge, Chunying Jia, Vince Calhoun, Tülay Adalı, Evrim Acar
For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood.
no code implementations • 28 Jun 2019 • José Camacho, Evrim Acar, Morten A. Rasmussen, Rasmus Bro
In this paper, we introduce the cross-product penalized component analysis (XCAN), a sparse matrix factorization based on the optimization of a loss function that allows a trade-off between variance maximization and structural preservation.
no code implementations • 7 Dec 2016 • Evrim Acar, Yuri Levin-Schwartz, Vince D. Calhoun, Tülay Adalı
Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity.
1 code implementation • 21 May 2010 • Daniel M. Dunlavy, Tamara G. Kolda, Evrim Acar
We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition.
no code implementations • 12 May 2010 • Evrim Acar, Tamara G. Kolda, Daniel M. Dunlavy, Morten Morup
In the presence of missing data, CP can be formulated as a weighted least squares problem that models only the known entries.
Numerical Analysis Numerical Analysis Data Analysis, Statistics and Probability G.1.3; G.1.6