Matrix Methods in Data Mining and Pattern Recognition, Second Edition

Lars Eldén

ISBN 10: 1611975859 / ISBN 13: 9781611975857

Published by SIAM - Society for Industrial and Applied Mathematics, 2019

About this Item

Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLY-NO ACCESS CODE, NO CD, Ships with Tracking. Seller Inventory # SKU0572747

Bibliographic Details

Title: Matrix Methods in Data Mining and Pattern ...

Publisher: SIAM - Society for Industrial and Applied Mathematics

Publication Date: 2019

Binding: paperback

Condition: Good

Edition: 2nd Edition

About this title

Synopsis:

This thoroughly revised second edition provides an updated treatment of numerical linear algebra techniques for solving problems in data mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how modern matrix methods can be applied in real life scenarios, and provides a set of tools that students can modify for a particular application.

Building on material from the first edition, the author discusses basic graph concepts and their matrix counterparts. He introduces the graph Laplacian and properties of its eigenvectors needed in spectral partitioning and describes spectral graph partitioning applied to social networks and text classification. Examples are included to help readers visualize the results. This new edition also presents matrix-based methods that underlie many of the algorithms used for big data.

The book provides a solid foundation to further explore related topics and presents applications such as classification of handwritten digits, text mining, text summarization, PageRank computations related to the Google search engine, and facial recognition. Exercises and computer assignments are available on a Web page that supplements the book.

Matrix Methods in Data Mining and Pattern Recognition, Second Edition is primarily for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course and graduate students in data mining and pattern recognition areas who need an introduction to linear algebra techniques.

About the Author:

Lars Eldén is a retired professor of scientific computing at Linköping University in Sweden, where he was head of the mathematics department and director of the National Supercomputer Centre. He is the author, along with L. Wittmeyer-Koch and H. Bruun Nielsen, of Introduction to Numerical Computation: Analysis and MATLAB Illustrations (Studentlitteratur AB, 2004).