Item type | Current location | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|
Books | 512.5 BIS (Browse shelf) | Available | 034977 |
Includes bibliographical references and indexes.
This book provides an elementary analytically inclined journey to a fundamental result of linear algebra: the Singular Value Decomposition (SVD). SVD is a workhorse in many applications of linear algebra to data science. Four important applications relevant to data science are considered throughout the book: determining the subspace that ""best'' approximates a given set (dimension reduction of a data set); finding the ""best'' lower rank approximation of a given matrix (compression and general approximation problems); the Moore-Penrose pseudo-inverse (relevant to solving least squares problems); and the orthogonal Procrustes problem (finding the orthogonal transformation that most closely transforms a given collection to a given configuration), as well as its orientation-preserving version.
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