Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/15320
Title: The alternating least-squares algorithm for CDPCA
Author: Macedo, E.
Freitas, A.
Keywords: Clustering
K-means
Principal component analysis
Issue Date: 2015
Publisher: Springer
Abstract: Clustering and Disjoint Principal Component Analysis (CDP CA) is a constrained principal component analysis recently proposed for clustering of objects and partitioning of variables, simultaneously, which we have implemented in R language. In this paper, we deal in detail with the alternating least-squares algorithm for CDPCA and highlight its algebraic features for constructing both interpretable principal components and clusters of objects. Two applications are given to illustrate the capabilities of this new methodology.
Peer review: yes
URI: http://hdl.handle.net/10773/15320
DOI: 10.1007/978-3-319-20352-2_12
ISBN: 978-3-319-20351-5
ISSN: 1865-0929
Appears in Collections:CIDMA - Capítulo de livro
PSG - Capítulo de livro

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