Low Rank Approximation : Algorithms, Implementation, Applications
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The work Low Rank Approximation : Algorithms, Implementation, Applications represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Low Rank Approximation : Algorithms, Implementation, Applications
Resource Information
The work Low Rank Approximation : Algorithms, Implementation, Applications represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Low Rank Approximation : Algorithms, Implementation, Applications
 Title remainder
 Algorithms, Implementation, Applications
 Statement of responsibility
 by Ivan Markovsky
 Subject

 Automatic control
 Computer scienceMathematics
 Control, Robotics, Mechatronics
 Electronic resources
 Image processing
 Mathematical Modeling and Industrial Mathematics
 Mathematical models
 Mechatronics
 Robotics
 Signal processing
 Signal, Image and Speech Processing
 Speech processing systems
 Symbolic and Algebraic Manipulation
 System theory
 Systems Theory, Control
 Artificial Intelligence
 Artificial intelligence
 Language
 eng
 Summary
 Matrix lowrank 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 lowrank 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 errorsinvariables identification; signal processing: harmonic retrieval, sumofdamped 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 handson experience with the theory and methods detailed. In addition, exercises and MATLAB® examples will assist the reader quickly to assimilate the theory on a chapterbychapter 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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 LC call number

 TJ210.2211.495
 TJ163.12
 Literary form
 non fiction
 Series statement
 Communications and Control Engineering,
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