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Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition
ISBN-13:
9781441998873
Veröffentl:
2011
Seiten:
236
Autor:
Haruo Yanai
Serie:
Statistics for Social and Behavioral Sciences
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
Beschreibung:
Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection of one subspace onto another, and the latter underlies principal component analysis, which seeks to find a subspace that captures the largest variability in the original space.
Fundamentals of Linear Algebra.- Projection Matrices.- Generalized Inverse Matrices.- Explicit Representations.- Singular Value Decomposition (SVD).- Various Applications.
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