Introduction to Multimodal and Tensor Data Analytics

Nathan Gaw (), Mostafa Reisi Gahrooei () and Panos M. Pardalos ()
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Nathan Gaw: Air Force Institute of Technology
Mostafa Reisi Gahrooei: University of Florida
Panos M. Pardalos: University of Florida

A chapter in Multimodal and Tensor Data Analytics for Industrial Systems Improvement, 2024, pp 1-6 from Springer

Abstract: Abstract In recent times, the pervasiveness of multimodal data, particularly within the scope of industrial engineering and operations research, has grown exponentially. A myriad of research has focused on integrating such data using various innovative techniques, highlighting numerous facets of multimodal data fusion, and unveiling a series of open challenges still awaiting solutions. This book sheds light on various methodologies centered on the fusion of multimodal data, particularly emphasizing the role of tensor-based data analytics. It offers a comprehensive perspective on real-world applications (e.g., manufacturing, healthcare, and renewable energy) while presenting several unique methodological domains, including functional and tensor data analysis, spatiotemporal data analytics, deep learning, federated/distributed learning, and integration of domain knowledge. The capabilities and distinguishing traits of these methods are also summarized in this introductory chapter. This section concludes with an outline that highlights the main contributions of this work and a discussion of the existing challenges and promising research avenues in the realm of tensor data analytics and multimodal data fusion.

Keywords: Multimodal data; Data fusion; Early fusion; Late fusion; Intermediate fusion (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-53092-0_1

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