ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2015
Multi-modal data fusion using source separation: Two effective models based on ICA and IVA and their properties.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed.
- Clinical Treatment Analysis and Imaging Study of Patients with Acute Angina in Cardiovascular Medicine.Journal of healthcare engineering · 2021Trial
- Enhancing biomedical signals through genetic algorithm optimized Exponentiated transmuted weibull denoising techniques.Scientific reports · 2026Article
- Neural fingerprint of the dark triad: Resting state BOLD power (fALFF) alterations in executive and default mode networks.Cognitive, affective & behavioral neuroscience · 2026Article
- Fusion of Multi-Task fMRI Data: Guided Solutions for IVA and Transposed IVA.Sensors (Basel, Switzerland) · 2026Article
- Multimodal data fusion in neuroscience: promises, challenges and future directions.IEEE signal processing magazine · 2025Article
- Generalized Coupled Matrix Tensor Factorization Method Based on Normalized Mutual Information for Simultaneous EEG-fMRI Data Analysis.Neuroinformatics · 2025Article
- Unmasking the Dark Triad: A Data Fusion Machine Learning Approach to Characterize the Neural Bases of Narcissistic, Machiavellian and Psychopathic Traits.The European journal of neuroscience · 2025Article
- Large-Scale Independent Vector Analysis (IVA-G) via Coresets.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2025Article
- A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies.Human brain mapping · 2024Article
- Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.Sensors (Basel, Switzerland) · 2024Article
- Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition.IEEE transactions on bio-medical engineering · 2024Article
- Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment.Applied sciences (Basel, Switzerland) · 2024Article
- A fronto-insular network underlies individual variations in anger expression and control.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- An exploratory study of brain temperature and choline abnormalities in temporal lobe epilepsy patients with depressive symptoms.Epilepsia open · 2023Article
- Data-driven multimodal fusion: approaches and applications in psychiatric research.Psychoradiology · 2023Review
- Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data.Neuroinformatics · 2023Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- A Review on Data Fusion of Multidimensional Medical and Biomedical Data.Molecules (Basel, Switzerland) · 2022Review
- Data Fusion-Based Musculoskeletal Synergies in the Grasping Hand.Sensors (Basel, Switzerland) · 2022Article
- Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.IEEE transactions on medical imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant for the given task is inherent to many problems we deal with today. Since, usually, very little is known about the actual interaction among the datasets, it is highly desirable to minimize the underlying assumptions. This has been the main reason for the growing importance of data-driven methods, and in particular of independent component analysis (ICA) as it provides useful decompositions with a simple generative model and using only the assumption of statistical independence. A recent extension of ICA, independent vector analysis (IVA) generalizes ICA to multiple datasets by exploiting the statistical dependence across the datasets, and hence, as we discuss in this paper, provides an attractive solution to fusion of data from multiple datasets along with ICA. In this paper, we focus on two multivariate solutions for multi-modal data fusion that let multiple modalities fully interact for the estimation of underlying features that jointly report on all modalities. One solution is the Joint ICA model that has found wide application in medical imaging, and the second one is the the Transposed IVA model introduced here as a generalization of an approach based on multi-set canonical correlation analysis. In the discussion, we emphasize the role of diversity in the decompositions achieved by these two models, present their properties and implementation details to enable the user make informed decisions on the selection of a model along with its associated parameters. Discussions are supported by simulation results to help highlight the main issues in the implementation of these methods.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.