eBooks :: Kembali

eBooks :: Kembali

Low rank approximation: algorithms, implementation, applications

Ivan Markovsky ([, Springer], 2012)

 Abstrak

Data approximation by low-complexity models details 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. Much of the text is devoted to describing the applications of the theory including, system and control theory, signal 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.

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Jenis Koleksi : eBooks
No. Panggil : e20410845
Entri utama-Nama orang :
Subjek :
Penerbitan : London: [, Springer], 2012
Sumber Pengatalogan: LibUI eng rda
Tipe Konten: text
Tipe Media: computer
Tipe Pembawa: online resource
Deskripsi Fisik:
Tautan: http://link.springer.com/book/10.1007%2F978-1-4471-2227-2
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