The Alternating Least-Squares Algorithm for CDPCA

被引：6
|
作者：
Macedo, Eloisa [1 ]
Freitas, Adelaide [1 ]
机构：
[1] Univ Aveiro, P-3800 Aveiro, Portugal
来源：
OPTIMIZATION IN THE NATURAL SCIENCES, EMC-ONS 2014 | 2015年 / 499卷
关键词：
Principal Component Analysis; Clustering; K-means;
D O I：
10.1007/978-3-319-20352-2_12
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：173 / 191
页数：19
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