Matrix methods in data mining and pattern recognition [electronic resource] / Lars Eldén
- Eldén, Lars, 1944-
- Philadelphia, PA : Society for Industrial and Applied Mathematics, 
- Copyright Date:
- Physical Description:
- x, 224 pages : illustrations ; 26 cm.
- Fundamentals of algorithms
- Restrictions on Access:
- License restrictions may limit access.
- Machine generated contents note: I.Linear algebra concepts and matrix decompositions -- 1.Vectors and matrices in data mining and pattern recognition -- 2.Vectors and matrices -- 3.Linear systems and least squares -- 4.Orthogonality -- 5.QR decomposition -- 6.Singular value decomposition -- 7.Reduced-rank least squares models -- 8.Tensor decomposition -- II.Data mining applications -- 10.Classification of handwritten digits -- 11.Text mining -- 12.Page ranking for a Web search engine -- 13.Automatic key word and key sentence extraction -- 14.Face recognition using tensor SVD -- III.Computing the matrix decompositions.
- "Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application." "The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful." --Book Jacket.
- 9780898716269 (pbk. : alk. paper)
0898716268 (pbk. : alk. paper)
- Bibliography Note:
- Includes bibliographical references (pages 209-216) and index.
View MARC record | catkey: 7858153