Low Rank Approximation [electronic resource] : Algorithms, Implementation, Applications / by Ivan Markovsky.

By: Markovsky, Ivan [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Series: Communications and Control Engineering: Publisher: London : Springer London : Imprint: Springer, 2012Description: X, 258 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781447122272Subject(s): Engineering | Algebra -- Data processing | Artificial intelligence | Systems theory | Engineering | Control, Robotics, Mechatronics | Systems Theory, Control | Symbolic and Algebraic Manipulation | Mathematical Modeling and Industrial Mathematics | Artificial Intelligence (incl. Robotics) | Signal, Image and Speech ProcessingAdditional physical formats: Printed edition:: No titleDDC classification: 629.8 LOC classification: TJ210.2-211.495TJ163.12Online resources: Click here to access online
Contents:
Introduction -- From Data to Models -- Applications in System and Control Theory -- Applications in Signal Processing -- Applications in Computer Algebra -- Applications in Machine Learing -- Subspace-type Algorithms -- Algorithms Based on Local Optimization -- Data Smoothing and Filtering -- Recursive Algorithms.
In: Springer eBooksSummary: Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications 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. Applications described include: system and control theory: approximate realization, model reduction, output error, and errors-in-variables identification; signal processing: harmonic retrieval, sum-of-damped exponentials, finite impulse response modeling, and array processing; machine learning: multidimensional scaling and recommender system; computer vision: algebraic curve fitting and fundamental matrix estimation; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; psychometrics for factor analysis; and computer algebra for approximate common divisor computation. Special knowledge from the respective application fields is not required. 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 the reader. This gives hands-on experience with the theory and methods detailed. In addition, exercises and MATLAB® examples will assist the reader quickly to assimilate the theory on a chapter-by-chapter basis.   Low Rank Approximation: Algorithms, Implementation, Applications is a broad survey of the theory and applications of its field which will be of direct interest to researchers in system identification, control and systems theory, numerical linear algebra and optimization. The supplementary problems and solutions render it suitable for use in teaching graduate courses in those subjects as well.
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Introduction -- From Data to Models -- Applications in System and Control Theory -- Applications in Signal Processing -- Applications in Computer Algebra -- Applications in Machine Learing -- Subspace-type Algorithms -- Algorithms Based on Local Optimization -- Data Smoothing and Filtering -- Recursive Algorithms.

Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications 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. Applications described include: system and control theory: approximate realization, model reduction, output error, and errors-in-variables identification; signal processing: harmonic retrieval, sum-of-damped exponentials, finite impulse response modeling, and array processing; machine learning: multidimensional scaling and recommender system; computer vision: algebraic curve fitting and fundamental matrix estimation; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; psychometrics for factor analysis; and computer algebra for approximate common divisor computation. Special knowledge from the respective application fields is not required. 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 the reader. This gives hands-on experience with the theory and methods detailed. In addition, exercises and MATLAB® examples will assist the reader quickly to assimilate the theory on a chapter-by-chapter basis.   Low Rank Approximation: Algorithms, Implementation, Applications is a broad survey of the theory and applications of its field which will be of direct interest to researchers in system identification, control and systems theory, numerical linear algebra and optimization. The supplementary problems and solutions render it suitable for use in teaching graduate courses in those subjects as well.

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