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Introduction to Multimodal and Tensor Data Analytics

Author

Listed:
  • Nathan Gaw

    (Air Force Institute of Technology)

  • Mostafa Reisi Gahrooei

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida)

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.

Suggested Citation

  • Nathan Gaw & Mostafa Reisi Gahrooei & Panos M. Pardalos, 2024. "Introduction to Multimodal and Tensor Data Analytics," Springer Optimization and Its Applications,, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-53092-0_1
    DOI: 10.1007/978-3-031-53092-0_1
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