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Mining Multimodal Big Data: Tensor Methods and Applications

Mining Multimodal Big Data: Tensor Methods and Applications

Sujoy Roy, Michael W. Berry
Copyright: © 2018 |Pages: 29
ISBN13: 9781522531425|ISBN10: 1522531424|EISBN13: 9781522531432
DOI: 10.4018/978-1-5225-3142-5.ch023
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MLA

Roy, Sujoy, and Michael W. Berry. "Mining Multimodal Big Data: Tensor Methods and Applications." Handbook of Research on Big Data Storage and Visualization Techniques, edited by Richard S. Segall and Jeffrey S. Cook, IGI Global, 2018, pp. 674-702. https://doi.org/10.4018/978-1-5225-3142-5.ch023

APA

Roy, S. & Berry, M. W. (2018). Mining Multimodal Big Data: Tensor Methods and Applications. In R. Segall & J. Cook (Eds.), Handbook of Research on Big Data Storage and Visualization Techniques (pp. 674-702). IGI Global. https://doi.org/10.4018/978-1-5225-3142-5.ch023

Chicago

Roy, Sujoy, and Michael W. Berry. "Mining Multimodal Big Data: Tensor Methods and Applications." In Handbook of Research on Big Data Storage and Visualization Techniques, edited by Richard S. Segall and Jeffrey S. Cook, 674-702. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3142-5.ch023

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Abstract

The last decade has witnessed exponential growth of data particularly in the fields of biomedicine, unstructured text processing and signal processing. There exist instances of data depicting simultaneous interactions amongst more than two types of entities. Such data are not readily amenable to matrix representation as matrices can show interactions between only two types of entities at a time. Tensors are multimodal extensions of matrices (a matrix can be thought of as 2-mode tensor), and tensor factorizations (decompositions) are multiway generalizations of matrix factorizations. This chapter provides an overview of tensor factorization methods as well as a literature review of selected applications in areas that are currently experiencing exponential data growth and likely of interest to a broad audience.

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