Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms)

丛 书 名：Fundamentals of algorithms

作     者：Lars Eldén 

I S B N：(纸本) 9780898716269 

出 版 社：Society for Industrial and Applied Mathematics 

出 版 年：2007年

页      数：x, 224 p. :页

主 题 词：Algebras Linear. Pattern recognition systems Data mining. Mathematical models. 

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 08[工学] 070104[理学-应用数学] 0701[理学-数学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

摘      要：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.