Search Results for author: Evrim Acar

Found 8 papers, 5 papers with code

An AO-ADMM approach to constraining PARAFAC2 on all modes

1 code implementation4 Oct 2021 Marie Roald, Carla Schenker, Rasmus Bro, Jeremy E. Cohen, Evrim Acar

We also apply our model to a real-world chromatography dataset, and show that constraining the evolving mode improves the interpretability of the extracted patterns.

PARAFAC2 AO-ADMM: Constraints in all modes

2 code implementations3 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.

A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings

2 code implementations19 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.

Tracing Network Evolution Using the PARAFAC2 Model

1 code implementation23 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.

Cross-product Penalized Component Analysis (XCAN)

no code implementations28 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.

Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia

no code implementations7 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.


Temporal Link Prediction using Matrix and Tensor Factorizations

1 code implementation21 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.

Link Prediction Tensor Decomposition

Scalable Tensor Factorizations for Incomplete Data

no code implementations12 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

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