Amazon cover image
Image from Amazon.com

Projection matrices, generalized inverse matrices, and singular value decomposition

By: Material type: TextTextSeries: Statistics for social and behavioral sciencePublication details: 2011 Springer New YorkDescription: xi, 234 pISBN:
  • 9781441998866
Subject(s): DDC classification:
  • 519.535 Y2P7
Summary: This book is about projections and SVD. A thorough discussion of generalized inverse (g-inverse) matrices is also given because it is closely related to the former. The book provides systematic and in-depth accounts of these concepts from a unified viewpoint of linear transformations finite dimensional vector spaces. More specially, it shows that projection matrices (projectors) and g-inverse matrices can be defined in various ways so that a vector space is decomposed into a direct-sum of (disjoint) subspaces. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition will be useful for researchers, practitioners, and students in applied mathematics, statistics, engineering, behaviormetrics, and other fields. (http://www.springer.com/statistics/book/978-1-4419-9886-6)
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Item location Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 28-B / Slot 1425 (0 Floor, East Wing) General Stacks 519.535 Y2P7 (Browse shelf(Opens below)) Available 174390

This book is about projections and SVD. A thorough discussion of generalized inverse (g-inverse) matrices is also given because it is closely related to the former. The book provides systematic and in-depth accounts of these concepts from a unified viewpoint of linear transformations finite dimensional vector spaces. More specially, it shows that projection matrices (projectors) and g-inverse matrices can be defined in various ways so that a vector space is decomposed into a direct-sum of (disjoint) subspaces. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition will be useful for researchers, practitioners, and students in applied mathematics, statistics, engineering, behaviormetrics, and other fields. (http://www.springer.com/statistics/book/978-1-4419-9886-6)

There are no comments on this title.

to post a comment.