| 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 | .Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation. Efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. Much of the text is devoted to describing the applications of the theory including: system and control theory; signal processing; computer algebra for approximate factorization and common divisor computation; computer vision for image deblurring and segmentation; machine learning for information retrieval and clustering; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; and psychometrics for factor analysis...Software implementation of the methods is given, making the theory directly applicable in practice. All numerical examples are included in demonstration files giving hands-on experience and exercises and MATLAB® examples assist in the assimilation of the theory.. |
| 出版日期 | Book 20121st edition |
| 关键词 | Control; Control Theory; Data Approximation; Hankel; Linear Algebra; Linear Models; Low-complexity Model; N |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4471-2227-2 |
| isbn_softcover | 978-1-4471-5836-3 |
| isbn_ebook | 978-1-4471-2227-2Series ISSN 0178-5354 Series E-ISSN 2197-7119 |
| issn_series | 0178-5354 |
| copyright | Springer-Verlag London Limited 2012 |
发表于 2025-3-21 20:06:00
