A database for publications published by researchers and students at SimulaMet.
Research area
Publication type
- All (362)
- Journal articles (132)
- Books (2)
- Edited books (1)
- Proceedings, refereed (162)
- Book chapters (6)
- Talks, keynote (1)
- PhD theses (5)
- Proceedings, non-refereed (2)
- Posters (7)
- Talks, invited (18)
- Talks, contributed (15) Remove Talks, contributed <span class="counter">(15)</span> filter
- Public outreach (3)
- Miscellaneous (8)
Talks, contributed
A Flexible Framework for Coupled Matrix/Tensor Factorizations
In TRICAP: Three-way methods In Chemistry And Psychology, 2022.Status: Published
A Flexible Framework for Coupled Matrix/Tensor Factorizations
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | TRICAP: Three-way methods In Chemistry And Psychology |
An AO-ADMM approach to constrained PARAFAC2
In Nordic AI Meet, 2022.Status: Published
An AO-ADMM approach to constrained PARAFAC2
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | Nordic AI Meet |
Analyzing postprandial metabolomics data using multiway models: A simulation study
In NuGOweek 2022 in Spain, 2022.Status: Published
Analyzing postprandial metabolomics data using multiway models: A simulation study
| Afilliation | Machine Learning |
| Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | NuGOweek 2022 in Spain |
Analyzing postprandial metabolomics data using multiway models: A simulation study
In Nordic Metabolomics 2022, Copenhagen, Denmark, 2022.Status: Published
Analyzing postprandial metabolomics data using multiway models: A simulation study
| Afilliation | Machine Learning |
| Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | Nordic Metabolomics 2022, Copenhagen, Denmark |
Analyzing postprandial metabolomics data using multiway models: A simulation study
In Norwegian Bioinformatics Days, Sundvolden, Norway, 2022.Status: Published
Analyzing postprandial metabolomics data using multiway models: A simulation study
| Afilliation | Machine Learning |
| Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | Norwegian Bioinformatics Days, Sundvolden, Norway |
Characterizing postprandial metabolomics response using multi-way data analysis
In Annual NORBIS Conference, 2022.Status: Published
Characterizing postprandial metabolomics response using multi-way data analysis
Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about the human metabolism by providing a better understanding of regulation of subgroups of metabolites (e.g., lipids) and variations in postprandial responses of subgroups of people, with the potential to ultimately advance precision medicine. However, characterizing postprandial metabolomics response and understanding group differences is a challenging task since it requires the analysis of large-scale metabolomics data from a large set of individuals containing measurements of a wide set of metabolites at multiple time points. Such data is in the form of a three-way array: subjects by metabolites by time points. The state-of-the-art analysis methods mainly focus on clustering temporal profiles relying on summaries of the data across subjects or univariate analysis techniques studying one metabolite at a time, and fail to associate subgroups of subjects and subsets of metabolites with the dynamic time profile simultaneously.
In this study, we use NMR (nuclear magnetic resonance) spectroscopy measurements of plasma samples (of over three hundred individuals from the COPSAC2000 cohort) collected at multiple time points during a challenge test. We use a multi-way analysis technique called the CANDECOMP/PARAFAC (CP) model to extract interpretable patterns from the time-resolved data. We compare the analysis of postprandial data, fasting state-corrected data and only fasting state data, and demonstrate the differences between different analysis approaches.
Our results show that the CP model reveals biologically meaningful patterns capturing how certain metabolite groups and their temporal profiles relate to various meta variables, in particular, BMI (body mass index), confirming already known biological knowledge as well as revealing new biological insights.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | Annual NORBIS Conference |
Fully Constrained PARAFAC2 with AO-ADMM
In SIAM Conference on Parallel Processing for Scientific Computing, 2022.Status: Published
Fully Constrained PARAFAC2 with AO-ADMM
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2022 |
| Location of Talk | SIAM Conference on Parallel Processing for Scientific Computing |
Talks, contributed
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
In Asilomar Conference on Signals, Systems, and Computers, 2021.Status: Published
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
An effective way of jointly analyzing data from multiple sources is through coupled matrix and tensor factorizations (CMTF). Different characteristics of datasets from multiple sources require to employ various regularizations, constraints, loss functions and different types of coupling structures between datasets. While existing algorithmic approaches for CMTF can incorporate constraints, linear couplings and different loss functions, none of them has been shown to achieve the flexibility to incorporate all. We propose a flexible algorithmic framework for coupled matrix and tensor factorizations, which utilizes Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM). The framework facilitates the use of a variety of constraints, loss functions and couplings with linear transformations. Numerical experiments on simulated datasets and real data from chemometrics and hyperspectral super-resolution demonstrate that the proposed approach is accurate, flexible and computationally efficient with comparable or better performance than available CMTF algorithms.
While we focus on CANDECOMP/PARAFAC (CP) –based CMTF models, we will also briefly discuss the use of an AO-ADMM based algorithmic approach for fitting a PARAFAC2 model. We demonstrate that the proposed algorithmic approach enables imposing constraints in all modes, which has been a challenge using the traditional alternating least squares-based algorithm used for PARAFAC2.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Talks, contributed |
| Year of Publication | 2021 |
| Location of Talk | Asilomar Conference on Signals, Systems, and Computers |
| Type of Talk | Invited Session Talk |
A new linearly implicit energy-preserving exponential method for conservative or dissipative systems.
In In Manifolds and Geometric Integration Colloquia, Norway, 2021.Status: Published
A new linearly implicit energy-preserving exponential method for conservative or dissipative systems.
In this work, we propose a linearly implicit exponential integrator that preserves the invariant or the Lyapunov functions for the conservative or dissipative systems by combining the idea of exponential integrators and discrete gradient methods. Numerical simulations are shown to confirm the conservative properties of the methods, and to demonstrate the efficiency of the methods when compared to other fully implicit schemes.
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2021 |
| Location of Talk | In Manifolds and Geometric Integration Colloquia, Norway |
Exploring dynamic metabolomics data with multiway data analysis: A simulation study
In Virtual conference. SIAM Conference on Applications of Dynamical Systems, 2021.Status: Published
Exploring dynamic metabolomics data with multiway data analysis: A simulation study
Analysis of dynamic metabolomics data sets holds the promise to improve our understanding of the underlying mechanisms in human metabolism. That is crucial to detect the changes in the metabolism that can potentially lead to diseases. Dynamic metabolomics data has more than two axes of variation, i.e., samples, metabolites and time. While such time-evolving multi-way data sets are collected more and more in recent years, revealing the underlying mechanisms and their dynamics from such data remains challenging.
This talk will focus on a systematic study demonstrating the advantages and limitations of multi-way data analysis (also known as tensor factorizations) in terms of analyzing dynamic metabolomics data. We study different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, a human cholesterol model, and generate data with different types of variation. Our numerical experiments demonstrate that despite the increasing complexity of the studied models, tensor factorization methods CANDECOMP/PARAFAC(CP) and PARAllel Profiles with LINear Dependences (PARALIND) can reveal the underlying mechanisms and their dynamics.
| Afilliation | Machine Learning |
| Project(s) | TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion, Department of Data Science and Knowledge Discovery |
| Publication Type | Talks, contributed |
| Year of Publication | 2021 |
| Location of Talk | Virtual conference |
| Publisher | SIAM Conference on Applications of Dynamical Systems |