Low Rank Approximation

Beschreibung

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; andcomputer 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.
This book details the theory, algorithms, and applications of structured low-rank approximation, and presents efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel and Sylvester structured problems and more.

Matrix low-rank approximation is intimately related to data modelling by a linear system; a problem that arises frequently in many different fields. This book is a comprehensive exposition of 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. 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 modelling, and array 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.

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.

Data Approximation by Low-complexity Models 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 electronic lecture slides, problems and solutions render it suitable for use in teaching graduate courses in those subjects as well.

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 from a sweeping field of applications

Supplementary electronic and class-based materials will aid tutors in presenting this material to their students

Autor*in

Ivan Markovsky

Themen in »Low Rank Approximation«

Control Control Theory Data Approximation Hankel Linear Algebra Linear Models Low-complexity Model Numerical Algorithms OJ0061 Sylvester System Identification System Theory Time-invariant System Toeplitz

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From the reviews:

“This is a carefully-elaborated monographic work on low rank approximation. It covers the state of the art in this field (key theoretical topics accompanied by the description of the associated algorithms) and discusses various classes of applications. The book provides a rigorous and self-contained material, including numerical examples implemented in MATLAB and a collection of relevant problems. The exposition corresponds to a postgraduate level.” (Octavian Pastravanu, Zentralblatt MATH, Vol. 1245, 2012)

“This book gently takes the reader from the basic ideas of LRA to the most critical concepts, with an adequate number of examples to explain things along the way. … Markovsky has presented LRA in a way that is unifying and cross-disciplinary. The pages abound with code, examples, applications, and problems, from which readers can pick according to their own interests and without the risk of losing the main thread of the book. … it is a good reference for students, practitioners, and researchers.” (Corrado Mencar, ACM Computing Reviews, December, 2012)

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