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Titlebook: Multivariate Data Analysis on Matrix Manifolds; (with Manopt) Nickolay Trendafilov,Michele Gallo Textbook 2021 The Editor(s) (if applicable

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書目名稱Multivariate Data Analysis on Matrix Manifolds
副標(biāo)題(with Manopt)
編輯Nickolay Trendafilov,Michele Gallo
視頻videohttp://file.papertrans.cn/642/641297/641297.mp4
概述Integrates multivariate data analysis with Riemannian geometry.Provides a unified treatment of several MDA techniques.Incorporates new tools and technology into current theory of MDA.Includes Manpot c
叢書名稱Springer Series in the Data Sciences
圖書封面Titlebook: Multivariate Data Analysis on Matrix Manifolds; (with Manopt) Nickolay Trendafilov,Michele Gallo Textbook 2021 The Editor(s) (if applicable
描述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
出版日期Textbook 2021
關(guān)鍵詞Multivariate Data Analysis; Matrix Manifolds; Data Science; Principal Component Analysis; Data matrix fa
版次1
doihttps://doi.org/10.1007/978-3-030-76974-1
isbn_softcover978-3-030-76976-5
isbn_ebook978-3-030-76974-1Series ISSN 2365-5674 Series E-ISSN 2365-5682
issn_series 2365-5674
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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2365-5674 and technology into current theory of MDA.Includes Manpot cThis 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 mani
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2365-5674 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, data978-3-030-76976-5978-3-030-76974-1Series ISSN 2365-5674 Series E-ISSN 2365-5682
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