Matrix Methods in Data Analysis

Author:   Maria Isabel Bueno Cachadina ,  Javier Perez Alvaro
Publisher:   Springer Nature Switzerland AG
ISBN:  

9783032113139


Publication Date:   05 April 2026
Format:   Hardback
Availability:   In Print   Availability explained
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Matrix Methods in Data Analysis


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Author:   Maria Isabel Bueno Cachadina ,  Javier Perez Alvaro
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9783032113139


ISBN 10:   303211313
Publication Date:   05 April 2026
Audience:   College/higher education ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Part I: Linear Algebra And Machine Learning.- Why Should We Care?.- What You May Have Learned Before..- Core Topics.- Supplementary Topics.- Part II: Matrix Multiplication And Partitioned Matrices.- Why Should We Care?.- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part III: Norms, Distances, And Similarities.- Why Should We Care?.- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part IV: The Four Fundamental Subspaces Of A Matrix, And Gram-Matrices.-  Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part V: The Lu Factorization Of A Matrix.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part VI: Orthogonality And The Qr Factorization.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part VII: Orthogonal Projections And The Least Squares Problem.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part VIII: Eigenvalues, Eigenvectors, And Algorithms.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part IX: Symmetric And Positive Definite Matrices.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part X: Singular Value Decomposition.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.-From The Classroom To Real Life.- Part XI: Nonnegative Matrices And Perron Theory.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Index.

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Author Information

Maria Isabel Bueno is a Teaching Professor at the University of California, Santa Barbara, where she has served since 2006. She holds a Ph.D. from Universidad Carlos III de Madrid. Her research focuses on linear algebra and numerical linear algebra. Javier Perez Alvaro is an Associate Professor at the University of Montana in Missoula, where he has served since 2017. He earned his Ph.D. from Universidad Carlos III de Madrid. His research focuses on numerical linear algebra and numerical analysis.

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