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Tensor and Multimodal Data Analysis

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Multimodal and Tensor Data Analytics for Industrial Systems Improvement

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 211))

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Abstract

In this book chapter, we provide a selective review of recent advances in tensor analysis and tensor modeling in statistics and machine learning. We then provide examples in health data science applications.

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Acknowledgements

Zeng’s research was partly supported by grant 12301365 from the National Natural Science Foundation of China and grant WK2040000075 from the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, China

    **g Zeng

  2. Department of Statistics, Florida State University, Tallahassee, FL, USA

    **n Zhang

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Correspondence to **g Zeng .

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  1. Department of Operational Sciences, Air Force Institute of Technology, Wright-Patterson AFB, OH, USA

    Nathan Gaw

  2. Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  3. Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Mostafa Reisi Gahrooei

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Zeng, J., Zhang, X. (2024). Tensor and Multimodal Data Analysis. In: Gaw, N., Pardalos, P.M., Gahrooei, M.R. (eds) Multimodal and Tensor Data Analytics for Industrial Systems Improvement. Springer Optimization and Its Applications, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-53092-0_5

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