Lars Eldén

Matrix Methods in Data Mining and Pattern Recognition

SIAM, 2007





  • Main
  • Preface (PDF)
  • Table of contents (PDF)
  • Chapter 1 (PDF)
  • Theory questions (PDF)
  • Errata (PDF)
  • Computer assignments
  • BIBTEX
  • Reviews
  • Matrix methods@SIAM
  • 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 applications discussed in the book are classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition.

    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.


    Lars Eldén, Professor of Scientific Computing
    Department of Mathematics
    Linköping University
    SE-581 83 Linköping
    Sweden

    Email: laeld AT math DOT liu DOT se