Matrix Methods in Data Mining and Pattern Recognition

Elden, Lars

ISBN 10: 0898716268 / ISBN 13: 9780898716269

Edité par Society for Industrial and Applied Mathematics, 2007

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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.

À propos de l?auteur: Lars Eldén is professor of numerical analysis at Linköping University in Sweden.

Détails bibliographiques

Titre : Matrix Methods in Data Mining and Pattern ...
Éditeur : Society for Industrial and Applied Mathematics
Date d'édition : 2007
Reliure : Couverture souple
Etat : good