An empirical comparison of two approaches for CDPCA in high-dimensional data

Modifed principal component analysis techniques, specially those yielding sparse solutions, are attractive due to its usefulness for interpretation purposes, in particular, in high-dimensional data sets. Clustering and disjoint principal component analysis (CDPCA) is a constrained PCA that promotes...

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Bibliographic Details
Main Author: Freitas, Adelaide (author)
Other Authors: Macedo, Eloísa (author), Vichi, Maurizio (author)
Format: article
Language:eng
Published: 2021
Subjects:
Principal component analysis
Clustering of objects
Partitioning of attributes
Semidefnite programming
Online Access:http://hdl.handle.net/10773/32545
Country:Portugal
Oai:oai:ria.ua.pt:10773/32545

Internet

http://hdl.handle.net/10773/32545

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