Автореферат (1150842), страница 4
Текст из файла (страница 4)
1. P. 49–62.6. Markovsky I. Structured low-rank approximation and its applications //Automatica. 2008. apr. Vol. 44, no. 4. P. 891–909.7. Dendrinos M., Bakamidis S., Carayannis G. Speech enhancement from noise:A regenerative approach // Speech Communication. 1991. feb. Vol. 10, no. 1.P. 45–57.8.
Golyandina N., Nekrutkin V., Zhigljavsky A. Analysis of Time SeriesStructure: SSA and Related Techniques. Chapman&Hall/CRC, 2001.9. Tufts D., Shah A. Estimation of a signal waveform from noisy data usinglow-rank approximation to a data matrix // IEEE Transactions on SignalProcessing. 1993. apr. Vol. 41, no. 4. P.
1716–1721.1610. Markovsky I. Low Rank Approximation: Algorithms, Implementation,Applications (Communications and Control Engineering). Springer, 2011.11. Ottaviani G., Spaenlehauer P.-J., Sturmfels B. Exact solutions in structuredlow-rank approximation // SIAM Journal on Matrix Analysis andApplications. 2014. Vol. 35, no.
4. P. 1521–1542.12. Usevich K., Markovsky I. Structured low-rank approximation as a rationalfunction minimization // IFAC Proceedings Volumes. 2012. Vol. 45, no. 16.P. 722–727.13. Usevich K., Markovsky I. Variable projection for affinely structured low-rankapproximation in weighted 2-norms // Journal of Computational and AppliedMathematics. 2014. Vol.
272. P. 430–448.14. Gillard J., Zhigljavsky A. Stochastic methods for Hankel structured lowrank approximation // Proceedings of 21th International Symposium onMathematical Theory of Networks and Systems. 2014. P. 961–964.15. Zhigljavsky A., Golyandina N., Gryaznov S. Deconvolution of a discreteuniform distribution // Stat Probabil Lett.
2016. Vol. 118. P. 37–44.16. Kukush A., Markovsky I., Van Huffel S. Consistency of the structured totalleast squares estimator in a multivariate errors-in-variables model // Journalof Statistical Planning and Inference. 2005. Vol. 133, no. 2. P. 315–358.17. The element-wise weighted total least-squares problem / Ivan Markovsky,Maria Luisa Rastello, Amedeo Premoli et al. // Computational Statistics &Data Analysis. 2006. Vol. 50, no.
1. P. 181–209.18. De Moor B. Total least squares for affinely structured matrices and the noisyrealization problem // IEEE Transactions on Signal Processing. 1994. Vol. 42,no. 11. P. 3104–3113..