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The Resource Low Rank Approximation : Algorithms, Implementation, Applications, by Ivan Markovsky, (electronic resource)
Low Rank Approximation : Algorithms, Implementation, Applications, by Ivan Markovsky, (electronic resource)
Resource Information
The item Low Rank Approximation : Algorithms, Implementation, Applications, by Ivan Markovsky, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item Low Rank Approximation : Algorithms, Implementation, Applications, by Ivan Markovsky, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.
 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
 Language
 eng
 Edition
 1st ed. 2012.
 Extent
 X, 258 p.
 Contents

 Introduction
 From Data to Models
 Applications in System and Control Theory
 Applications in Signal Processing
 Applications in Computer Algebra
 Applications in Machine Learing
 Subspacetype Algorithms
 Algorithms Based on Local Optimization
 Data Smoothing and Filtering
 Recursive Algorithms
 Isbn
 9781447122272
 Label
 Low Rank Approximation : Algorithms, Implementation, Applications
 Title
 Low Rank Approximation
 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
 http://library.link/vocab/creatorName
 Markovsky, Ivan
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
 Hz0IL1O5_vs
 Image bit depth
 0
 LC call number

 TJ210.2211.495
 TJ163.12
 Literary form
 non fiction
 Series statement
 Communications and Control Engineering,
 http://library.link/vocab/subjectName

 Automatic control
 Robotics
 Mechatronics
 System theory
 Computer scienceMathematics
 Mathematical models
 Artificial intelligence
 Signal processing
 Image processing
 Speech processing systems
 Control, Robotics, Mechatronics
 Systems Theory, Control
 Symbolic and Algebraic Manipulation
 Mathematical Modeling and Industrial Mathematics
 Artificial Intelligence
 Signal, Image and Speech Processing
 Label
 Low Rank Approximation : Algorithms, Implementation, Applications, by Ivan Markovsky, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 Introduction  From Data to Models  Applications in System and Control Theory  Applications in Signal Processing  Applications in Computer Algebra  Applications in Machine Learing  Subspacetype Algorithms  Algorithms Based on Local Optimization  Data Smoothing and Filtering  Recursive Algorithms
 Dimensions
 unknown
 Edition
 1st ed. 2012.
 Extent
 X, 258 p.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9781447122272
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9781447122272
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9781447122272
 Label
 Low Rank Approximation : Algorithms, Implementation, Applications, by Ivan Markovsky, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 Introduction  From Data to Models  Applications in System and Control Theory  Applications in Signal Processing  Applications in Computer Algebra  Applications in Machine Learing  Subspacetype Algorithms  Algorithms Based on Local Optimization  Data Smoothing and Filtering  Recursive Algorithms
 Dimensions
 unknown
 Edition
 1st ed. 2012.
 Extent
 X, 258 p.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9781447122272
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9781447122272
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9781447122272
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
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