Stress Functions for Supervised Dimensionality Reduction

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 6 in Nonlinear Dimensionality Reduction Techniques, 2022, pp 119-156 from Springer

Abstract: Abstract In the general case, Dimensionality Reduction (DR) is an unsupervised task. Indeed, it does not necessitate data annotations, as opposed to classification for which the desired output must be provided for a training set. Yet, DR is sometimes applied to datasets for which such annotations are available (e.g., class-information). In this context, supervised dimensionality reduction methods seek to take advantage of that information to improve the representation of the data structure.

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_6

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

DOI: 10.1007/978-3-030-81026-9_6

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 ().