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Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way
The so-called block-term decomposition (BTD) tensor model, especially in its rank-$(L_r, L_r, 1)$ version, has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of \emph{blocks} of rank higher than one, a scenario encountered in numerous and diverse applications.
Early soft and flexible fusion of EEG and fMRI via tensor decompositions
The fusion methods reported so far ignore the underlying multi-way nature of the data in at least one of the modalities and/or rely on very strong assumptions about the relation of the two datasets.
Assisted Dictionary Learning for fMRI Data Analysis
Extracting information from functional magnetic resonance (fMRI) images has been a major area of research for more than two decades.
Higher-Order Block Term Decomposition for Spatially Folded fMRI Data
The growing use of neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality.
Blind Source Separation: Fundamentals and Recent Advances (A Tutorial Overview Presented at SBrT-2001)
Blind source separation (BSS), i. e., the decoupling of unknown signals that have been mixed in an unknown way, has been a topic of great interest in the signal processing community for the last decade, covering a wide range of applications in such diverse fields as digital communications, pattern recognition, biomedical engineering, and financial data analysis, among others.
