Principal Component Analysis (Part 2)

Kohei Adachi ()
Additional contact information
Kohei Adachi: Osaka University, Graduate School of Human Sciences

Chapter Chapter 6 in Matrix-Based Introduction to Multivariate Data Analysis, 2020, pp 81-94 from Springer

Abstract: Abstract In this chapter, principal component analysis (PCA)Principal Component Analysis (PCA) is reformulated. The loss function to be minimized is the same as that in the previous chapter, but the constraintsConstraint for the matrices are different.

Date: 2020
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:978-981-15-4103-2_6

Ordering information: This item can be ordered from
http://www.springer.com/9789811541032

DOI: 10.1007/978-981-15-4103-2_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 ().