Self-Consistency and Principal Component Analysis
作者:
Thaddeus Tarpey,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1999)
卷期:
Volume 94,
issue 446
页码: 456-467
ISSN:0162-1459
年代: 1999
DOI:10.1080/01621459.1999.10474140
出版商: Taylor & Francis Group
关键词: Bootstrap;Elliptical distribution;κ-means clustering;Principal point;Principal curve;Self-consistent point;Spherical distribution;Symmetric distribution
数据来源: Taylor
摘要:
I examine the self-consistency of a principal component axis; that is, when a distribution is centered about a principal component axis. A principal component axis of a random vectorXis self-consistent if each point on the axis corresponds to the mean ofXgiven thatXprojects orthogonally onto that point. A large class of symmetric multivariate distributions are examined in terms of self-consistency of principal component subspaces. Elliptical distributions are characterized by the preservation of self-consistency of principal component axes after arbitrary linear transformations. A “lack-of-fit” test is proposed that tests for self-consistency of a principal axis. The test is applied to two real datasets.
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