Multimodal Data Fusion: An Overview of Methods, Challenges and Prospects

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dc.contributor.authorLahat, Dana
dc.contributor.authorAdali, Tulay
dc.contributor.authorJutten, Christian
dc.date.accessioned2018-05-25T14:45:51Z
dc.date.available2018-05-25T14:45:51Z
dc.date.issued2015
dc.descriptioncopyright 2015 IEEEen_US
dc.description.abstractIn various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term “modality” for each such acquisition framework. Due to the rich characteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or “challenges,” are common to multiple domains. This paper deals with two key issues: “why we need data fusion” and “how we perform it.” The first issue is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second issue, “diversity” is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the data sets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects, and the opportunities that it holds.en_US
dc.description.sponsorshipThis work is supported by the project CHESS, 2012-ERC-AdG-320684 (D. Lahat and Ch. Jutten) and by the grants NSF-IIS 1017718 and NSF-CCF 1117056 (T. Adalı). GIPSA-Lab is a partner of the LabEx PERSYVAL-Lab (ANR–11-LABX-0025).en_US
dc.description.urihttps://ieeexplore.ieee.org/document/7214350/en_US
dc.format.extent26 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/M2M90260V
dc.identifier.citationLahat, Dana, Tulay Adali, and Christian Jutten. Multimodal Data Fusion: An Overview of Methods, Challenges and Prospects. Proceedings of IEEE Vol. 103, no. 9, pp. 1449-1477, Sep. 2015. DOI: 10.1109/JPROC.2015.2460697en_US
dc.identifier.uri10.1109/JPROC.2015.2460697
dc.identifier.urihttp://hdl.handle.net/11603/10867
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectdata integrationen_US
dc.subjectElectroencephalographyen_US
dc.subjectsensorsen_US
dc.subjectmulltimodel sensorsen_US
dc.subjectlaser radaren_US
dc.subjectsynthetic aperture radaren_US
dc.titleMultimodal Data Fusion: An Overview of Methods, Challenges and Prospectsen_US
dc.typeTexten_US

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