000 04258nam a22005535i 4500
001 978-1-4471-2227-2
003 DE-He213
005 20140220083234.0
007 cr nn 008mamaa
008 130607s2012 xxk| s |||| 0|eng d
020 _a9781447122272
_9978-1-4471-2227-2
024 7 _a10.1007/978-1-4471-2227-2
_2doi
050 4 _aTJ210.2-211.495
050 4 _aTJ163.12
072 7 _aTJFM
_2bicssc
072 7 _aTJFD
_2bicssc
072 7 _aTEC004000
_2bisacsh
072 7 _aTEC037000
_2bisacsh
082 0 4 _a629.8
_223
100 1 _aMarkovsky, Ivan.
_eauthor.
245 1 0 _aLow Rank Approximation
_h[electronic resource] :
_bAlgorithms, Implementation, Applications /
_cby Ivan Markovsky.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2012.
300 _aX, 258 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aCommunications and Control Engineering,
_x0178-5354
505 0 _aIntroduction -- 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.
520 _aMatrix 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.
650 0 _aEngineering.
650 0 _aAlgebra
_xData processing.
650 0 _aArtificial intelligence.
650 0 _aSystems theory.
650 1 4 _aEngineering.
650 2 4 _aControl, Robotics, Mechatronics.
650 2 4 _aSystems Theory, Control.
650 2 4 _aSymbolic and Algebraic Manipulation.
650 2 4 _aMathematical Modeling and Industrial Mathematics.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aSignal, Image and Speech Processing.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447122265
830 0 _aCommunications and Control Engineering,
_x0178-5354
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-2227-2
912 _aZDB-2-ENG
999 _c100630
_d100630