TY - JOUR
T1 - Definition and properties ofm-dimensional n-principal points
AU - Matsuura, Shun
AU - Kurata, Hiroshi
N1 - Funding Information:
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions, which led to significant improvements in this article. Kurata’s portion of this work was supported by JSPS KAKENHI Grant Number 23700341. Kurata’s portion of this work was supported by JSPS KAKENHI Grant Number 20243016, 21500272.
PY - 2013
Y1 - 2013
N2 - In this article, we introduce the notion of "m-dimensional n-principal points," which is a generalization of the notion of n-principal points. A set of m-dimensional n-principal points of a distribution is defined as a set of n points that optimally represents the distribution in terms of mean squared distance subject to the condition that the dimension of the linear subspace spanned by the n points is at most m. Its properties and connections to principal components are investigated for elliptically symmetric distributions and a location mixture of spherically symmetric distributions.
AB - In this article, we introduce the notion of "m-dimensional n-principal points," which is a generalization of the notion of n-principal points. A set of m-dimensional n-principal points of a distribution is defined as a set of n points that optimally represents the distribution in terms of mean squared distance subject to the condition that the dimension of the linear subspace spanned by the n points is at most m. Its properties and connections to principal components are investigated for elliptically symmetric distributions and a location mixture of spherically symmetric distributions.
KW - Elliptically symmetric distribution
KW - Location mixture
KW - Mean squared distance
KW - Principal points
KW - Spherically symmetric distribution
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U2 - 10.1080/03610926.2011.581791
DO - 10.1080/03610926.2011.581791
M3 - Article
AN - SCOPUS:84872018191
SN - 0361-0926
VL - 42
SP - 267
EP - 282
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 2
ER -