Data Science Context

Sylvain Lespinats, Benoit Colange and Denys Dutykh
Additional contact information
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 1 in Nonlinear Dimensionality Reduction Techniques, 2022, pp 1-30 from Springer

Abstract: Abstract This chapter positions Dimensionality Reduction (DR) in the broader context of data science, considering both its use as an automated pre-processing tool extracting variable (manifold learning) for other automated tasks (e.g., classification, clustering, regression), and as a mapping technique allowing to visualize multidimensional data in a low-dimensional space (spatialization). In the process, it also introduces some general tools of data analysis such as distances computations and intrinsic Data set dimensionality estimation, which are also of paramount importance when performing the Dimensionality Reduction.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-81026-9_1

Ordering information: This item can be ordered from
http://www.springer.com/9783030810269

DOI: 10.1007/978-3-030-81026-9_1

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().