Data Fusion based on Coupled Matrix and Tensor Factorizations

Recent technological advances enable us to collect huge amounts of data from multiple sources; however, extracting meaningful information remains to be the main challenge. In complex problems, the structure we are looking for is often buried in the data. In those cases, in particular, looking at the data from different aspects; in other words, jointly analyzing data from multiple sources, i.e., data fusion (also called multi-block, multi-view or multi-set data analysis), increases the chances of capturing the hidden patterns. For instance, in metabolomics, biological fluids are measured using a variety of analytical techniques such as Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS) and Nuclear Magnetic Resonance (NMR) Spectroscopy with an ultimate goal of identifying chemicals related to certain conditions such as diseases. Data measured using different analytical methods are often complementary and their fusion enhances knowledge discovery.

We develop mathematical models and algorithms for data fusion. Our earlier work has focused on coupled matrix and tensor factorizations and proved useful especially in recommender system applications. With the goal of fusing data sets for pattern/biomarker discovery, we have later focused on structure-revealing data fusion models that can identify shared - unshared factors in coupled data sets.

Data fusion is a challenging task due to the heterogeneity of the data sets (matrices vs. higher-order tensors, different noise characteristics) and there are many open research questions. We have been working on those research questions with the goal of addressing the challenges in real applications, in particular, in omics data fusion and multimodal neuroimaging data analysis.

 PI: Evrim Acar

• JODA: Joint Data Analysis for Enhanced Knowledge Discovery in Metabolomics (funded by the Danish Council for Independent Research | Technology and Production Sciences and Sapere Aude Program under the projects 11-116328 and 11-120947 between March 2012 - December 2016, Denmark)
• Multimodal Neuroimaging Data Fusion (funded by Simula Metropolitan Center for Digital Engineering, Norway)

 COLLABORATORS:

• Rasmus Bro, Faculty of Science, University of Copenhagen, Denmark
• Mathias Nilsson, School of Chemistry, The University of Manchester, UK
• Lars Ove Dragsted, Faculty of Science, University of Copenhagen, Denmark
• Gozde Gurdeniz, Faculty of Science, University of Copenhagen, Denmark
• Michael Saunders, Stanford University, Stanford, CA
• Anne Tjønneland, Danish Cancer Society, Denmark
• Tormod Næs, Nofima, Norway
• Tamara G. Kolda, Sandia National Labs, Livermore, CA
• A. Taylan Cemgil, Bogazici University, Istanbul, Turkey
• Beyza Ermis, Bogazici University, Istanbul, Turkey
• Bulent Yener, Rensselaer Polytechnic Institute, Troy, NY
• Vagelis Papalexakis, University of California Riverside, CA
• Age K. Smilde, University of Amsterdam, Netherlands
• Tulay Adali, University of Maryland Baltimore County, MD
• Urban J. Wunsch, Chalmers University of Technology, Goteborg, Sweden

SOFTWARE: The MATLAB CMTF Toolbox

• Version 1.1  (Dec. 2014)
• Version 1.0  (April 2013)

DATA:

• Prototypical Experimental Coupled Data Sets

RELATED PUBLICATIONS
Coupled Matrix and Tensor Factorizations (Models and Algorithms)

• Common and Distinct Components in Data Fusion (A. K. Smilde, I. Mage, T. Naes, T. Hankemeier, M. A. Lips, H. A. L. Kiers, E. Acar, and R. Bro), Journal of Chemometrics, 31: e2900, 2017.
• Structure-Revealing Data Fusion (E. Acar, E. E. Papalexakis, G. Gurdeniz, M. A. Rasmussen, A. J. Lawaetz, M. Nilsson, and R. Bro), BMC Bioinformatics, 15: 239, 2014.
• A Flexible Modeling Framework for Coupled Matrix and Tensor Factorizations (E. Acar, M. Nilsson, and M. Saunders), EUSIPCO, 2014.
• Understanding Data Fusion Within the Framework of Coupled Matrix and Tensor Factorizations (E. Acar, M. A. Rasmussen, F. Savorani, T. Naes, and R. Bro), Chemometrics and Intelligent Laboratory Systems,  129: 53-63, 2013.
• Optimal Weight Learning for Coupled Tensor Factorization with Mixed Divergences (U. Simsekli, B. Ermis, A. T. Cemgil, and E. Acar), EUSIPCO, 2013.
•  All-at-once Optimization for Coupled Matrix and Tensor Factorizations (E. Acar, T.G.Kolda, and D.M. Dunlavy), KDD Workshop on Mining and Learning with Graphs, 2011.

Metabolomics

• Forecasting Chronic Diseases using Data Fusion (E. Acar, G. Gurdeniz, F. Savorani, L. Hansen, A. Olsen, A. Tjønneland, L. O. Dragsted, and R. Bro), Journal of Proteome Research, 2017.
• Data Fusion in Metabolomics using Coupled Matrix and Tensor Factorizations (E. Acar, R. Bro, and A. K. Smilde), Proceedings of the IEEE, 103: 1602-1620, 2015.
• Structure Revealing Data Fusion Model with Applications in Metabolomics (E. Acar, A. J. Lawaetz, M. A. Rasmussen, and R. Bro), IEEE EMBC, pp. 6023-6026, 2013.
• Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics (E. Acar, G. Gurdeniz, M. Rasmussen, D. Rago, L. Dragsted, and R. Bro), International Journal of Knowledge Discovery in Bioinformatics, 3:22-43, 2012.
• Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics (E. Acar, G. Gurdeniz, M. Rasmussen, D. Rago, L. Dragsted, R. Bro), ICDM Workshop on Biological Data Mining and its Applications in Healthcare, pp. 1-8, 2012.

Neuroscience

• Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data (E. Acar, C. Schenker, Y. Levin-Schwartz, V. D. Calhoun, and T. Adali), Frontiers in Neuroscience, 13: 416, 2019.
• ACMTF for Fusion of Multi-modal Neuroimaging Data and Identification of Biomarkers (E. Acar, Y. Levin-Schwartz, V. D. Calhoun, and T. Adali), EUSIPCO, pp. 643-647, 2017.
• Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes (E. Acar, Y. Levin-Schwartz, V. D. Calhoun, and T. Adali), IEEE ISCAS, pp. 314-317, 2017.
• Incorporating Higher Dimensionality in Joint Decomposition of EEG and fMRI (W. Swinnen, B. Hunyadi, E. Acar, S. Van Huffel, and M. De Vos), EUSIPCO, 2014.

Social Network Analysis

• Link Prediction in Heterogeneous Data via Generalized Coupled Tensor Factorization (B. Ermis, E. Acar, and A. T. Cemgil), Data Mining and Knowledge Discovery, 29(1): 203-236, 2015.
• Link Prediction via Generalized Coupled Tensor Factorisation (B. Ermis, E. Acar, and A. T. Cemgil), ECML/PKDD Workshop on Collective Learning and Inference on Structured Data, 2012.

Bioinformatics

• Coupled Analysis of in Vitro and Histology Tissue Samples to Quantify Structure-Function Relationship (E. Acar, G. E. Plopper, and B. Yener), PLoS One , 7(3): e32227, 2012.

Environmental Sciences

• The Molecular Fingerprint of Fluorescent Natural Organic Matter Offers Insight into Biogeochemical Sources and Diagenetic State (U. J. Wunsch, E. Acar, B. P. Koch, K. R. Murphy, P. Schmitt-Kopplin, and C. A. Stedmon), Analytical Chemistry, 90(24): 14188-14197, 2018.

TALKS AT CONFERENCES/WORKSHOPS & SEMINARS:

• 13th International Symposium on Medical Information and Communication Technology, Oslo, Norway, May 8-10, 2019 (Invited Speaker)
• University of Oslo, Oslo, Norway, March 14, 2019 (Invited Seminar)
• COBRA: 5th Conference on Constraint-Based Reconstruction and Analysis, Seattle, WA, October 14-16, 2018 (Invited Speaker)
• 14th International Conference on Latent Variable Analysis and Signal Separation, University of Surrey, Guildford, UK, July 2-6, 2018 (Invited Tutorial)
• Three-way Methods in Chemistry and Psychology (TRICAP), Angel Fire, NM, June 11-15, 2018
• University of Granada, Granada, Spain, February 22-23, 2018 (Invited Seminar)
• Simula Research Lab, Fornebu, Norway, September 15, 2017
• EUSIPCO, Kos, Greece, August 28-September 1, 2017 (Refereed Paper)
• Sandia National Labs, Livermore, CA, April 24, 2017
• IBM Research, Zurich, Switzerland, March 13, 2017
• Steno Diabetes Center, Copenhagen, Denmark, October 5, 2016.
• 13th NuGOweek: Phenotypes and Prevention - The Interplay of Genes, Life-style factors and Gut Environment (Copenhagen, Denmark), September 5-8, 2016 (Invited Talk).
• 20th Conference of the International Linear Algebra Society (ILAS), Leuven, Belgium, July 11-15, 2016 (Invited Minisymposium Talk)
• SIAM Conf. Parallel Processing for Scientific Computing, Paris, France, April 12-15, 2016 (Invited Minisymposium Talk)
• Dagstuhl Perspectives Workshop: Tensor Computing for Internet of Things, Schloss Dagstuhl, Germany, April 10-13, 2016 (Organizer/Speaker)
• Metabolomics Young Investigators Network Meeting, University of Copenhagen, March 30, 2016 (Invited Talk)
• The University of Edinburgh, Edinburgh, UK, March 17, 2016 (Invited Seminar)
• Workshop on Tensor Decompositions and Applications, Leuven, Belgium, January 21, 2016
• Ozyegin University, Istanbul, Turkey, December 29, 2015
• Nofima, Ås, Norway, November 5, 2015
• Yeditepe University, Istanbul, Turkey, September 8, 2015
• Bogazici University, Istanbul, Turkey, August 20, 2015
• JODA Workshop on data fusion methods and applications, Copenhagen, Denmark, August 12, 2015 (Organizer/Speaker)
• SCIX, Reno- Tahoe, Sept. 28- Oct.3, 2014 (Invited Minisymposium Talk)
• EUSIPCO, Lisbon, Portugal, September 2, 2014 (Refereed Paper)
• COMPSTAT, Geneva, Switzerland, August 19-22, 2014 (Invited Minisymposium Talk)
• KU Leuven, Belgium, June 2, 2014 (Invited Seminar)
• Arctic Analysis, Ilulissat, Greenland, March 10-14, 2014
• ERCIM 2013, University of London, UK, December 15, 2013 (Invited Minisymposium Talk)
• Gipsa-Lab, Grenoble, France, November 8, 2013 (Invited Seminar)
• Xerox Research Centre Europe, Grenoble, France, November 7, 2013 (Invited Seminar)
• Kadir Has University, Istanbul, Turkey, October 23, 2013
• Biomedical Engineering Society (BMES) Annual Meeting, Seattle, WA, September 28, 2013 (Invited Talk)
• IEEE EMBC, Osaka, Japan, July 7, 2013 (Refereed Paper)
• ICME Colloquium, Stanford University, CA, June 3, 2013
• SIAM Conference on Computational Science and Engineering (CSE), Boston, MA, Feb. 25-March 1, 2013 (Minisymposium Organizer/Speaker)
• SPIE Medical Imaging: Digital Pathology Conference, Orlando, FL, Feb. 10, 2013 (Refereed Paper)
• SISTA Workshop on Tensors and Data Analysis, KU Leuven, Belgium, December 13, 2012 (Invited Talk)
• ICDM Workshop on Biological Data Mining and its Applications in Healthcare, Brussels, Belgium, December 10, 2012 (Refereed Paper)
• SIAM Conference on Applied Linear Algebra, Valencia, Spain, June 18-22, 2012 (Invited Minisymposium Talk)
• Three-way Methods in Chemistry and Psychology (TRICAP), Bruges, Belgium, June 2-7, 2012
• Linear Algebra/Optimization Seminar , Stanford University, CA, May 10, 2012
• Sandia National Labs, Livermore, CA, May 7, 2012

WORKSHOPS/MINISYMPOSIA:

• Dagstuhl Perspectives Workshop: Tensor Computing for Internet of Things, Schloss Dagstuhl, Germany, April 10-13, 2016 (Organizers: Evrim Acar, Animashree Anandkumar, Lenore Mullin, Volker Tresp, Sebnem Rusitschka)
• Mini-workshop on data fusion methods and applications, August 12, 2015, University of Copenhagen, Frederiksberg C, Denmark.
• Two-part minisymposia on data fusion methods based on matrix and tensor factorizations at SIAM Conference on Computational Science and Engineering (CSE), Feb. 25-March 1, 2013, Boston, MA (Organizers: Evrim Acar, A. Taylan Cemgil, Rasmus Bro)

LITERATURE SURVEYS:

• Data Fusion based on coupled matrix/tensor factorizations
• Metabolomics (cancer, nutrition, and fusion)

COURSES:

• Data fusion as part of the multi-way data analysis course, University of Copenhagen, June 13, 2019
• Copenhagen School of Chemometrics, May 25-26, 2016
• ODIN Course on Data Fusion, November 25, 2015

 Maintained by: Evrim Acar