Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms, Series Number 4)

Eldén, Lars

ISBN 10: 0898716268 / ISBN 13: 9780898716269

Published by Society for Industrial and Applied Mathematics, 2007

About this Item

This item shows limited signs of wear overall with minor scuffs or cosmetic blemishes. No curled corners, bent covers or damage to dust jackets. No highlighting writing in pages. Digital codes may not be included and have not been tested to be redeemable and or active. Please note that all items are donated goods and are in used condition. Orders shipped Monday through Friday! Your purchase helps put people to work and learn life skills to reach their full potential. Orders shipped Monday through Friday. Your purchase helps put people to work and learn life skills to reach their full potential. Thank you!

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Bibliographic Details

• Title: Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms, Series Number 4)
• Publisher: Society for Industrial and Applied Mathematics
• Publication Date: 2007
• Language: English
• ISBN 10: 0898716268
• ISBN 13: 9780898716269
• Binding: Soft cover
• Condition: very_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.