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Matrix methods in data mining and pattern recognition / Lars Eldén

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Fundamentals of algorithms ; 4Publisher: Philadelphia, Pa. : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), 2007Description: Online-Ressource (1 electronic text x, 224 p.)ISBN:
  • 9780898716269
  • 0898716268
  • 9780898718867
  • 9780898718867
Subject(s): Additional physical formats: 0898716268 | Erscheint auch als Druck-AusgabeDDC classification:
  • 05.74 220
MSC: MSC: *68T10 | 68T05 | 68-01 | 65F10 | 65F15 | 65F25 | 68U10 | 68U15 | 65Y15RVK: RVK: SK 220 | SK 915 | ST 330 | ST 530LOC classification:
  • QA76.9.D343
DOI: DOI: 10.1137/1.9780898718867Online resources: Additional physical formats: Also available in print version.
Contents:
Includes bibliographical references (p. 209-216) and index
I Linear Algebra Concepts and Matrix DecompositionsVectors and Matrices in Data Mining and Pattern Recognition -- Vectors and Matrices -- Linear Systems and Least Squares -- Orthogonality -- QR Decomposition -- Singular Value Decomposition -- Reduced Rank Least Squares Models -- Tensor Decomposition -- Clustering and Non-Negative Matrix Factorization -- II Data Mining Applications -- Classification of Handwritten Digits -- Text Mining -- Page Ranking for a Web Search Engine -- Automatic Key Word and Key Sentence Extraction -- Face Recognition Using Tensor SVD -- III Computing the Matrix Decompositions -- Computing Eigenvalues and Singular Values.
I Linear Algebra Concepts and Matrix Decompositions -- Vectors and Matrices in Data Mining and Pattern Recognition -- Vectors and Matrices -- Linear Systems and Least Squares -- Orthogonality -- QR Decomposition -- Singular Value Decomposition -- Reduced Rank Least Squares Models -- Tensor Decomposition -- Clustering and Non-Negative Matrix Factorization -- II Data Mining Applications -- Classification of Handwritten Digits -- Text Mining -- Page Ranking for a Web Search Engine -- Automatic Key Word and Key Sentence Extraction -- Face Recognition Using Tensor SVD -- III Computing the Matrix Decompositions -- Computing Eigenvalues and Singular Values.
Summary: I Linear Algebra Concepts and Matrix Decompositions -- Vectors and Matrices in Data Mining and Pattern Recognition -- Vectors and Matrices -- Linear Systems and Least Squares -- Orthogonality -- QR Decomposition -- Singular Value Decomposition -- Reduced Rank Least Squares Models -- Tensor Decomposition -- Clustering and Non-Negative Matrix Factorization -- II Data Mining Applications -- Classification of Handwritten Digits -- Text Mining -- Page Ranking for a Web Search Engine -- Automatic Key Word and Key Sentence Extraction -- Face Recognition Using Tensor SVD -- III Computing the Matrix Decompositions -- Computing Eigenvalues and Singular ValuesSummary: Includes bibliographical references (p. 209-216) and indexSummary: This application-oriented book describes how modern matrix methods can be used to solve problems in data mining and pattern recognition, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular applicationPPN: PPN: 680116443Package identifier: Produktsigel: ZDB-72-SIA
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