[1]	I. Markovsky. Dynamic measurement. In Data-driven filtering and control design: Methods and applications, chapter 6, pages 97--108. IET, 2019. [ bib \| DOI \| pdf \| Abstract ]
[2]	I. Markovsky, A. Fazzi, and N. Guglielmi. Applications of polynomial common factor computation in signal processing. In Latent Variable Analysis and Signal Separation, Lecture Notes in Computer Science, pages 99--106. Springer, 2018. [ bib \| DOI \| pdf \| Abstract ]
[3]	I. Markovsky and P.-L. Dragotti. Using structured low-rank approximation for sparse signal recovery. In Latent Variable Analysis and Signal Separation, Lecture Notes in Computer Science, pages 479--487. Springer, 2018. [ bib \| DOI \| pdf \| software \| Abstract ]
[4]	I. Markovsky. System identification in the behavioral setting: A structured low-rank approximation approach. In E. Vincent et al., editors, Latent Variable Analysis and Signal Separation, volume 9237 of Lecture Notes in Computer Science, pages 235--242. Springer, 2015. [ bib \| DOI \| pdf \| Abstract ]
[5]	I. Markovsky. Rank constrained optimization problems in computer vision. In A. Argyriou J. Suykens, M. Signoretto, editor, Regularization, Optimization, Kernels, and Support Vector Machines, Pattern Recognition, chapter 13, pages 293--312. Chapman & Hall/CRC Machine Learning, 2014. [ bib \| DOI \| pdf ]
[6]	I. Markovsky and K. Usevich. Nonlinearly structured low-rank approximation. In Yun Raymond Fu, editor, Low-Rank and Sparse Modeling for Visual Analysis, pages 1--22. Springer, 2014. [ bib \| DOI \| pdf \| Abstract ]
[7]	I. Markovsky. Algorithms and literate programs for weighted low-rank approximation with missing data. volume 3, chapter 12, pages 255--273. Springer, 2011. [ bib \| DOI \| pdf \| software ]
[8]	I. Markovsky, A. Amann, and S. Van Huffel. Application of filtering methods for removal of resuscitation artifacts from human ECG signals. In L. Wang, H. Garnier, and T. Jakeman, editors, System Identification, Environmental Modelling, and Control System Design. Springer, 2009. [ bib \| DOI \| pdf \| software ]
[9]	I. Markovsky and S. Van Huffel. On weighted structured total least squares. In I. Lirkov, S. Margenov, and J. Waśniewski, editors, Large-Scale Scientific Computing, volume 3743 of Lecture Notes in Computer Science, pages 695--702. Springer--Verlag, 2006. [ bib \| DOI \| pdf ]
[10]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistent estimation of an ellipsoid with known center. In J. Antoch, editor, Comput. Stat. (COMPSTAT), pages 1369--1376. Physica--Verlag, 2004. [ bib \| DOI \| .ps.gz ]
[11]	A. Kukush, I. Markovsky, and S. Van Huffel. On consistent estimators in linear and bilinear multivariate errors-in-variables models. In S. Van Huffel and P. Lemmerling, editors, Total Least Squares and Errors-in-Variables Modeling: Analysis, Algorithms and Applications, pages 155--164. Kluwer, 2002. [ bib \| DOI \| .ps.gz \| Abstract ]

This file was generated by bibtex2html 1.98.