Multivariate Data Analysis on Matrix Manifolds

Language: English

Published by Springer, Palgrave Macmillan Sep 2021, 2021

3030769739 / 9783030769734

Series: Book 9 of 11 - Springer Series in the Data Sciences

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This item is printed on demand - Print on Demand Titel. 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 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 472 pp. Englisch.…

Title
Multivariate Data Analysis on Matrix Manifolds
Author
Nickolay Trendafilov
Publisher
Springer, Palgrave Macmillan Sep 2021
Publication year
2021
Condition
Neu
Binding
Buch
Language
English
ISBN 10
3030769739
ISBN 13
9783030769734
Item weight
955 grams
Dimensions
241x160x30 mm
Series
Book 9 of 11: Springer Series in the Data Sciences

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