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Multivariate Data Analysis Matrix by Trendafilov Nickolay (31 results)
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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)
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Multivariate Data Analysis on Matrix Manifolds : With Manopt
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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)
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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
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030769739 ISBN 13: 9783030769734
Language: English
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Add to basketHardback. Condition: New. 2021 ed. 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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Hardcover. Condition: Brand New. Brand New! Fast Delivery, Delivery within 7-12 working Day Only, USA Edition Original Edition. Excellent Quality, Printing in English Language, Quick delivery by FEDEX & DHL. USPS & UPS Act. Our courier service is not available at PO BOX& APO BOX. Ship from India & United States.
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Papeback. Condition: Brand New. Brand New! Fast Delivery, Delivery within 7-12 working Day Only, USA Edition Original Edition. Excellent Quality, Printing in English Language, Quick delivery by FEDEX & DHL. USPS & UPS Act. Our courier service is not available at PO BOX& APO BOX. Ship from India & United States.
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Multivariate Data Analysis on Matrix Manifolds
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030769739 ISBN 13: 9783030769734
Language: English
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Add to basketHardback. Condition: New. 2021 ed. 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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Multivariate Data Analysis on Matrix Manifolds
Published by Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3030769763 ISBN 13: 9783030769765
Language: English
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Add to basketKartoniert / Broschiert. Condition: 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 .
