UNRAVELING INDEPENDENT COMPONENT ANALYSIS FOR TENSOR-VALUED DATA

Joni Oja Nordhausen

doi:10.55640/gmj-abc114

Open Access

UNRAVELING INDEPENDENT COMPONENT ANALYSIS FOR TENSOR-VALUED DATA

https://doi.org/10.55640/gmj-abc114

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Joni Oja Nordhausen ¹ ,

⁴ Department of Mathematics and Statistics, University of Turku, Finland

Abstract

In the realm of data analysis, the exploration of independent component analysis (ICA) for tensor-valued data represents a burgeoning area of research. Unlike traditional scalar or vector data, tensor-valued data capture complex relationships and structures across multiple dimensions. Independent component analysis offers a powerful framework for decomposing tensor-valued data into statistically independent components, revealing underlying patterns and dependencies that may remain obscured in raw data representations. This paper delves into the application of ICA techniques specifically tailored for tensor-valued data, exploring theoretical foundations, algorithmic implementations, and practical considerations. Through a comprehensive review and analysis, we elucidate the potential of ICA in uncovering hidden structures and sources of variability within tensor-valued datasets across diverse domains.

Keywords

Independent component analysis, tensor-valued data, multidimensional data analysis

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How to Cite

Joni Oja Nordhausen. (2023). UNRAVELING INDEPENDENT COMPONENT ANALYSIS FOR TENSOR-VALUED DATA. Global Multidisciplinary Journal, 2(03), 01-07. https://doi.org/10.55640/gmj-abc114

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UNRAVELING INDEPENDENT COMPONENT ANALYSIS FOR TENSOR-VALUED DATA

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