Matrix Methods in Data Mining and Pattern Recognition
Lars Eldn
Sold by World of Books Inc, Montgomery, IL, U.S.A.
AbeBooks Seller since 23 March 2026
Used - Soft cover
Condition: Used - Good
Ships within U.S.A.
Quantity: 1 available
Add to basketSold by World of Books Inc, Montgomery, IL, U.S.A.
AbeBooks Seller since 23 March 2026
Condition: Used - Good
Quantity: 1 available
Add to basketSeveral 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 I, linear algebra techniques are applied to data mining problems. Part I 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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