Multivariate Data Analysis Matrix by Trendafilov Nickolay (35 results)
Language: English
Published by Springer, 2021
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Language: English
Published by Springer, 2021
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Published by Springer, 2021
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Language: English
Published by Springer Nature Switzerland AG, CH, 2021
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Hardback. 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 the…m 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.
Language: English
Published by Springer, 2021
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Language: English
Published by Springer, 2021
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Language: English
Published by Springer, 2021
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
Language: English
Published by Springer, 2021
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Language: English
Published by Springer, 2021
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Language: English
Published by Springer Nature Switzerland AG, Cham, 2022
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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.
Language: English
Published by Springer, 2021
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Language: English
Published by Springer, 2021
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
Language: English
Published by Springer, 2021
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Hardcover. Condition: Brand New. 450 pages. 9.75x6.50x1.00 inches. In Stock.
Language: English
Published by Springer, 2021
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Language: English
Published by Springer, 2021
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Language: English
Published by Springer, 2021
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
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Published by Springer 2021-09-16, 2021
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
Language: English
Published by Springer, 2022
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Language: English
Published by Springer, 2021
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Multivariate Data Analysis on Matrix Manifolds: (with Manopt) (Springer Series in the Data Sciences)
Language: English
Published by Springer, 2022
- Softcover
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Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2021
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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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Language: English
Published by Springer Nature Switzerland AG, CH, 2021
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Hardback. Condition: New. 2021 ed.
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Language: English
Published by Springer, 2022
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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, juer…gen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Language: English
Published by Springer, 2022
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Taschenbuch. 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 de…fined 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.
Language: English
Published by Springer Nature Switzerland AG, Cham, 2022
- Softcover
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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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Language: English
Published by Springer, Palgrave Macmillan, 2021
- Hardcover
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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 b…y 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.
Language: English
Published by Springer, 2022
- Softcover
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Language: English
Published by Springer, 2021
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Language: English
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