Matrix Methods in Data Mining and Pattern Recognition

Lars Eldn

ISBN 10: 0898716268 / ISBN 13: 9780898716269

Published by Society for Industrial and Applied Mathematics, 2007

About this Item

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. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB. In Part I, linear algebra techniques are applied to data mining problems. Part I is a brief introduction to eigenvalue and singular value algorithms. The applications discussed include classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.

Seller Inventory # CIN0898716268G

Bibliographic Details

• Title: Matrix Methods in Data Mining and Pattern Recognition
• Publisher: Society for Industrial and Applied Mathematics
• Publication Date: 2007
• Language: English
• ISBN 10: 0898716268
• ISBN 13: 9780898716269
• Binding: Paperback
• Condition: Good

About this title

Synopsis

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. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed include classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.

About the Author

Lars Eldén is professor of numerical analysis at Linköping University in Sweden.