000			a
999			_c33130 _d33130
008			240427b xxu||||| |||| 00| 0 eng d
020			_a978-1-4704-6332-8
082			_a512.5 _bBIS
100			_aBisgard, James
245			_aAnalysis and linear algebra : the singular value decomposition and applications
260			_bAmerican Mathematical Society, _c2021 _aProvidence :
300			_axviii, 217 p.; _bill., _c21 cm
365			_b59.00 _c$ _d86.30
490			_aStudent mathematical library
504			_aIncludes bibliographical references and indexes.
520			_aThis 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.
650			_aMathematical analysis
650			_aNormed Vector Spaces
650			_aSymmetric matrix
650			_aSpectral Theorem
942			_2ddc _cBK