Matrix methods in data mining and pattern recognition / / Lars (1944-....) Eldén (2019)

Public ISBD Titre : Matrix methods in data mining and pattern recognition / Type de document : texte imprimé Auteurs : Lars (1944-....) Eldén, Auteur Mention d'édition : 2nd édition Editeur : Philadelphia : Society for Industrial and Applied Mathematics Année de publication : 2019 Collection : Fundamentals of algorithms Importance : XIII, 229 p. Présentation : ill. Format : 26 cm ISBN/ISSN/EAN : 978-1-61197-585-7 Note générale : Bibliogr. p. 217-224. - Index Langues : Anglais (eng) Mots-clés : Data Mining Pattern recognition systems Pattern recognition (computing) -- Mathematical models Exploration de données Systèmes de reconnaissance de formes Reconnaissance de formes (informatique) -- Modèles mathématiques Index. décimale : 004.93'1 Reconnaissance des formes. Appariement de formes Résumé : 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. Note de contenu : Au sommaire : 1. Vectors and Matrices in Data Mining and Pattern Recognition 2. Vectors and Matrices 3. Linear Systems and Least Squares 4. Orthogonality 5. QR Decomposition 6. Singular Value Decomposition 7. Reduced-Rank Least Squares Models 8. Tensor Decomposition 9. Clustering and Nonnegative Matrix Factorization 10. Classification of Handwritten Digits 11. Text Mining 12. Page Ranking for a Web Search Engine 13. Automatic Key Word and Key Sentence Extraction 14. Face Recognition Using Tensor SVD 15. Computing Eigenvalues and Singular Values Matrix methods in data mining and pattern recognition / [texte imprimé] / Lars (1944-....) Eldén, Auteur  . -  2nd édition . - Philadelphia : Society for Industrial and Applied Mathematics, 2019 . - XIII, 229 p. : ill. ; 26 cm. - (Fundamentals of algorithms) . ISBN : 978-1-61197-585-7 Bibliogr. p. 217-224. - Index Langues : Anglais (eng) Mots-clés : Data Mining Pattern recognition systems Pattern recognition (computing) -- Mathematical models Exploration de données Systèmes de reconnaissance de formes Reconnaissance de formes (informatique) -- Modèles mathématiques Index. décimale : 004.93'1 Reconnaissance des formes. Appariement de formes Résumé : 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. Note de contenu : Au sommaire : 1. Vectors and Matrices in Data Mining and Pattern Recognition 2. Vectors and Matrices 3. Linear Systems and Least Squares 4. Orthogonality 5. QR Decomposition 6. Singular Value Decomposition 7. Reduced-Rank Least Squares Models 8. Tensor Decomposition 9. Clustering and Nonnegative Matrix Factorization 10. Classification of Handwritten Digits 11. Text Mining 12. Page Ranking for a Web Search Engine 13. Automatic Key Word and Key Sentence Extraction 14. Face Recognition Using Tensor SVD 15. Computing Eigenvalues and Singular Values

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
058914	004.93'1 ELD	Papier	Bibliothèque Centrale	Informatique	Disponible	Consultation sur place