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Multivariate Data Analysis Matrix by Trendafilov Nickolay (35 results)
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Condition: As New. Unread book in perfect condition.
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Multivariate Data Analysis on Matrix Manifolds
Book 9 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030769739 ISBN 13: 9783030769734
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. 2021 ed.
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
Book 9 of 11: Springer Series in the Data SciencesSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Multivariate Data Analysis on Matrix Manifolds : With Manopt
Book 9 of 11: Springer Series in the Data SciencesSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Multivariate Data Analysis on Matrix Manifolds (Paperback)
Book 9 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030769763 ISBN 13: 9783030769765
Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Multivariate Data Analysis on Matrix Manifolds : With Manopt
Book 9 of 11: Springer Series in the Data SciencesSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Hardcover. Condition: Brand New. 450 pages. 9.75x6.50x1.00 inches. In Stock.
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
Book 9 of 11: Springer Series in the Data SciencesSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
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Hardcover. Condition: New. ISBN:9783030769734.
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Multivariate Data Analysis on Matrix Manifolds
Book 9 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10: 3030769739 ISBN 13: 9783030769734
Condition: New. 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.This graduate-level textbook aims to give a unified p.
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International EditionHardcover. Condition: New. Brand New ! Fast Delivery "International Edition " and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Multivariate Data Analysis on Matrix Manifolds
Book 9 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030769739 ISBN 13: 9783030769734
Hardback. Condition: New. 2021 ed.
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Taschenbuch. Condition: Neu. Multivariate Data Analysis on Matrix Manifolds | (with Manopt) | Nickolay Trendafilov (u. a.) | Taschenbuch | Springer Series in the Data Sciences | xx | Englisch | 2022 | Springer | EAN 9783030769765 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Multivariate Data Analysis on Matrix Manifolds (Paperback)
Book 9 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030769763 ISBN 13: 9783030769765
Paperback. Condition: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Multivariate Data Analysis on Matrix Manifolds : (with Manopt)
Book 9 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer, Palgrave Macmillan, 2021
ISBN 10: 3030769739 ISBN 13: 9783030769734
Buch. Condition: 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.
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Condition: new. Questo è un articolo print on demand.
