| 书目名称 | Principal Component Analysis | | 编辑 | I. T. Jolliffe | | 视频video | | | 丛书名称 | Springer Series in Statistics | | 图书封面 |  | | 描述 | Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of | | 出版日期 | Book 19861st edition | | 关键词 | Eigenvalue; Finite; Matrix; Statistica; computation; computer; eigenvector; factor analysis; form; principal | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4757-1904-8 | | isbn_ebook | 978-1-4757-1904-8Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer-Verlag New York 1986 |
The information of publication is updating
|
|
发表于 2025-3-21 18:24:45
