| 书目名称 | Multivariate Data Analysis on Matrix Manifolds |
| 副标题 | (with Manopt) |
| 编辑 | Nickolay Trendafilov,Michele Gallo |
| 视频video | |
| 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 |
| 丛书名称 | Springer Series in the Data Sciences |
| 图书封面 |  |
| 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 |
| 关键词 | Multivariate Data Analysis; Matrix Manifolds; Data Science; Principal Component Analysis; Data matrix fa |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-76974-1 |
| isbn_softcover | 978-3-030-76976-5 |
| isbn_ebook | 978-3-030-76974-1Series ISSN 2365-5674 Series E-ISSN 2365-5682 |
| issn_series | 2365-5674 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 19:55:31
