Abstract
Data fusion is the joint analysis of multiple inter-related datasets that provide complementary views of the same phenomenon. The process of correlating and fusing information from multiple sources generally allows more accurate inferences than those that the analysis of a single dataset can yield. Data fusion is a multifaceted concept with clear advantages but at the same time with numerous challenges that need to be carefully addressed. Coupled tensor decompositions have been proved successful in a plethora of data fusion applications, in view of their uniqueness properties and their unique ability to discover and fuse latent multidimensional information from inter-linked datasets. The aim of this chapter is to provide a brief overview of the data fusion concept and its advantages and challenges, with a discussion of coupled tensor decomposition models and methods, showing their power in solving data fusion tasks, as compared to matrix decomposition-based approaches. A few relevant applications are overviewed, particularly the fusion of electroencephalography and functional magnetic resonance imaging data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
