Matrix Methods in Data Mining and Pattern Recognition
By (author) Lars Elden
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Matrix Methods in Data Mining and Pattern Recognition by Lars Elden
Book DescriptionSeveral 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.
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Book DetailsISBN: 9780898716269
(247mm x 174mm x 16mm)
Imprint: Society for Industrial & Applied Mathematics,U.S.
Publisher: Society for Industrial & Applied Mathematics,U.S.
Publish Date: 12-Jul-2007
Country of Publication: United States
Books By Author Lars Elden
Introduction to Numerical Computation, Paperback (January 2004)» View all books by Lars Elden
Describes the numerical methods for error analysis, differentiation, interpolation and approximation, and the solution of non-linear equations, linear systems of algebraic equations and systems of ordinary differential equations. This title is intended as a textbook for a course in scientific computation at an advanced undergraduate level.
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Author Biography - Lars Elden
Lars Elden is professor of numerical analysis at Linkoping University in Sweden.
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