Articoli correlati a Multivariate Data Analysis on Matrix Manifolds: (with...
- EditoreSpringer
- Data di pubblicazione2022
- ISBN 10 3030769763
- ISBN 13 9783030769765
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero edizione1
- Numero di pagine472
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Multivariate Data Analysis on Matrix Manifolds
Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. Codice articolo 26396371820
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Multivariate Data Analysis on Matrix Manifolds
Da: Basi6 International, Irving, TX, U.S.A.
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Codice articolo ABEJUNE24-379636
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Multivariate Data Analysis on Matrix Manifolds
Da: Majestic Books, Hounslow, Regno Unito
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Condizione: New. Codice articolo 400005299
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Multivariate Data Analysis on Matrix Manifolds
Da: Biblios, Frankfurt am main, HESSE, Germania
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Multivariate Data Analysis on Matrix Manifolds : (with Manopt)
Editore:
Springer International Publishing, 2022
ISBN 10: 3030769763
ISBN 13: 9783030769765
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Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This graduate-level textbook aims to give a unified presentation and solution of several commonly used techniques for multivariate data analysis (MDA). Unlike similar texts, it treats the MDA problems as optimization problems on matrix manifolds defined by the MDA model parameters, allowing them to be solved using (free) optimization software Manopt. The book includes numerous in-text examples as well as Manopt codes and software guides, which can be applied directly or used as templates for solving similar and new problems. The first two chapters provide an overview and essential background for studying MDA, giving basic information and notations. Next, it considers several sets of matrices routinely used in MDA as parameter spaces, along with their basic topological properties. A brief introduction to matrix (Riemannian) manifolds and optimization methods on them with Manopt complete the MDA prerequisite. The remaining chapters study individual MDA techniques in depth. The number ofexercises complement the main text with additional information and occasionally involve open and/or challenging research questions. Suitable fields include computational statistics, data analysis, data mining and data science, as well as theoretical computer science, machine learning and optimization. It is assumed that the readers have some familiarity with MDA and some experience with matrix analysis, computing, and optimization. Codice articolo 9783030769765
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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Condizione: New. Codice articolo ABLIING23Mar3113020029441
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
Da: Ria Christie Collections, Uxbridge, Regno Unito
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Multivariate Data Analysis on Matrix Manifolds
Editore:
Springer International Publishing Sep 2022, 2022
ISBN 10: 3030769763
ISBN 13: 9783030769765
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Taschenbuch
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This graduate-level textbook aims to give a unified presentation and solution of several commonly used techniques for multivariate data analysis (MDA). Unlike similar texts, it treats the MDA problems as optimization problems on matrix manifolds defined by the MDA model parameters, allowing them to be solved using (free) optimization software Manopt. The book includes numerous in-text examples as well as Manopt codes and software guides, which can be applied directly or used as templates for solving similar and new problems. The first two chapters provide an overview and essential background for studying MDA, giving basic information and notations. Next, it considers several sets of matrices routinely used in MDA as parameter spaces, along with their basic topological properties. A brief introduction to matrix (Riemannian) manifolds and optimization methods on them with Manopt complete the MDA prerequisite. The remaining chapters study individual MDA techniques in depth. The number of exercises complement the main text with additional information and occasionally involve open and/or challenging research questions. Suitable fields include computational statistics, data analysis, data mining and data science, as well as theoretical computer science, machine learning and optimization. It is assumed that the readers have some familiarity with MDA and some experience with matrix analysis, computing, and optimization. 472 pp. Englisch. Codice articolo 9783030769765
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Multivariate Data Analysis on Matrix Manifolds
Editore:
Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3030769763
ISBN 13: 9783030769765
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This graduate-level textbook aims to give a unified presentation and solution of several commonly used techniques for multivariate data analysis (MDA). Unlike similar texts, it treats the MDA problems as optimization problems on matrix manifolds defined by . Codice articolo 693974418
Quantità: Più di 20 disponibili