Intrinsic Dimensionality

Sylvain Lespinats, Benoit Colange and Denys Dutykh
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Sylvain Lespinats: Grenoble Alpes University, National Institute of Solar Energy (INES)
Benoit Colange: Grenoble Alpes University, National Institute of Solar Energy (INES)
Denys Dutykh: Université Grenoble Alpes, Université Savoie Mont Blanc, Campus Scientifique, CNRS - LAMA UMR 5127

Chapter Chapter 2 in Nonlinear Dimensionality Reduction Techniques, 2022, pp 31-44 from Springer

Abstract: Abstract High dimensional data are subject to the curse of dimensionality defined in Sect. 2.1, which hinders their analysis. Yet, in practice, data may be assumed to live in a manifold whose dimensionality is lower than that of the data space dimensionality. The effective dimensionality of data is called intrinsic dimensionality. Its estimation is detailed Sect. 2.2.

Date: 2022
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DOI: 10.1007/978-3-030-81026-9_2

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