| 书目名称 | Low-Rank Approximation |
| 副标题 | Algorithms, Implemen |
| 编辑 | Ivan Markovsky |
| 视频video | |
| 概述 | Provides the reader with an analysis tool which is more generally applicable than the commonly-used total least squares.Shows the reader solutions to the problem of data modelling by linear systems fr |
| 丛书名称 | Communications and Control Engineering |
| 图书封面 |  |
| 描述 | This book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory with a range of applications from systems and control theory to psychometrics being described. Special knowledge of the application fields is not required..The second edition of /Low-Rank Approximation/ is a thoroughly edited and extensively rewritten revision. It contains new chapters and sections that introduce the topics of:.• variable projection for structured low-rank approximation;.• missing data estimation;.• data-driven filtering and control;.• stochastic model representation and identification;.• identification of polynomial time-invariant systems; and.• blind identification with deterministic input model..The book is complemented by a software implementation of the methods presented, which makes the theory directly applicable in practice. In particular, all numerical examples in the book are included in demonstration files and can be reproduced by t |
| 出版日期 | Book 2019Latest edition |
| 关键词 | Data Approximation; Linear Algebra; Linear Models; Low-complexity Model; Numerical Algorithms; System Ide |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-3-319-89620-5 |
| isbn_softcover | 978-3-030-07817-1 |
| isbn_ebook | 978-3-319-89620-5Series ISSN 0178-5354 Series E-ISSN 2197-7119 |
| issn_series | 0178-5354 |
| copyright | Springer International Publishing AG, part of Springer Nature 2019 |
发表于 2025-3-21 19:00:53
