000 03901nam a22003735i 4500
001 287686
003 MX-SnUAN
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008 150903s2012 xxk| o |||| 0|eng d
020 _a9781447122272
_99781447122272
024 7 _a10.1007/9781447122272
_2doi
035 _avtls000339524
039 9 _a201509030317
_bVLOAD
_c201404300401
_dVLOAD
_y201402060937
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTJ210.2-211.495
100 1 _aMarkovsky, Ivan.
_eautor
_9316995
245 1 0 _aLow Rank Approximation :
_bAlgorithms, Implementation, Applications /
_cby Ivan Markovsky.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2012.
300 _ax, 256 páginas 64 ilustraciones, 55 ilustraciones en color.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aCommunications and Control Engineering,
_x0178-5354
500 _aSpringer eBooks
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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781447122265
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-2227-2
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c287686
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