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Book
ISBN: 3030769747 3030769739 Year: 2021 Publisher: Cham, Switzerland : Springer,
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Anàlisi multivariable --- Varietats (Matemàtica) --- Anàlisi multivariant --- Estadística matemàtica --- Matrius (Matemàtica) --- Anàlisi de conglomerats --- Anàlisi de correspondències (Estadística) --- Anàlisi discriminant --- Modelització multiescala --- Models d'equacions estructurals --- Anàlisi conjunt (Màrqueting) --- Varietats analítiques --- Geometria diferencial --- Topologia --- Varietats topològiques --- Catàstrofes (Matemàtica) --- Geometria espectral --- Homeomorfismes --- Subvarietats (Matemàtica) --- Topologia de baixa dimensió --- Tor (Geometria) --- Varietats complexes --- Varietats de Calabi-Yau --- Varietats de Grassmann --- Varietats diferenciables --- Varietats de Kähler --- Varietats simplèctiques --- Multivariate analysis. --- Multivariate analysis --- Manifolds (Mathematics) --- Computer programs. --- Geometry, Differential --- Topology --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices
Digital
ISBN: 9783030769741 9783030769758 9783030769765 9783030769734 Year: 2021 Publisher: Cham Springer International Publishing
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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 of exercises 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. .
Algebraic geometry --- Differential geometry. Global analysis --- Mathematics --- Computer. Automation --- topologie (wiskunde) --- informatica --- statistiek --- wiskunde --- geometrie
Book
ISBN: 9783030769741 9783030769758 9783030769765 9783030769734 Year: 2021 Publisher: Cham Springer International Publishing :Imprint: Springer
Abstract | Keywords | Export | Availability | Bookmark
Loading...Choose an application
- Reference Manager
- EndNote
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Algebraic geometry --- Differential geometry. Global analysis --- Mathematics --- Computer. Automation --- topologie (wiskunde) --- informatica --- statistiek --- wiskunde --- geometrie
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