Low rank approximation: algorithms, implementation, applications
Ivan Markovsky ([, Springer], 2012)
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| 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. |
 Low Rank Approximation Algorithms, Implementation, Applications, Ivan Markovsky 2012.pdf :: Unduh
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| 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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| No. Panggil | No. Barkod | Ketersediaan |
|---|---|---|
| e20410845 | TERSEDIA |
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| Tidak ada ulasan pada koleksi ini: 20410845 |
