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Publications of the SLRA project
Books
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[1]
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I. Markovsky.
Low Rank Approximation: Algorithms, Implementation,
Applications.
Springer, 2012.
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Journal papers
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[1]
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K. Usevich and I. Markovsky.
Variable projection methods for approximate (greatest) common divisor
computations.
Theoretical Computer Science, 2016.
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[2]
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I. Markovsky.
On the most powerful unfalsified model for data with missing values.
Systems & Control Letters, 2016.
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[3]
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I. Markovsky.
Comparison of adaptive and model-free methods for dynamic
measurement.
IEEE Signal Proc. Letters, 22(8):1094-1097, 2015.
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[4]
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I. Markovsky and R. Pintelon.
Identification of linear time-invariant systems from multiple
experiments.
IEEE Trans. Signal Process., 63(13):3549-3554, 2015.
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[5]
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K. Usevich and I. Markovsky.
Adjusted least squares fitting of algebraic hypersurfaces.
Linear Algebra Appl., 2015.
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[6]
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I. Markovsky.
An application of system identification in metrology.
Control Engineering Practice, 43:85-93, 2015.
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[7]
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N. Golyandina, A. Korobeynikov, A. Shlemov, and K. Usevich.
Multivariate and 2D extensions of singular spectrum analysis with
the Rssa Package.
Journal of Statistical Software, 67(2), 2015.
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[8]
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P. Dreesen, M. Ishteva, and J. Schoukens.
Decoupling multivariate polynomials using first-order information and
tensor decompositions.
SIAM J. Matrix Anal. Appl., 36(2):864-879, 2015.
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[9]
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K. Usevich and I. Markovsky.
Variable projection for affinely structured low-rank approximation in
weighted 2-norms.
J. Comput. Appl. Math., 272:430-448, 2014.
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[10]
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I. Markovsky and K. Usevich.
Software for weighted structured low-rank approximation.
J. Comput. Appl. Math., 256:278-292, 2014.
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[11]
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I. Markovsky.
Recent progress on variable projection methods for structured
low-rank approximation.
Signal Processing, 96PB:406-419, 2014.
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[12]
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K. Usevich and I. Markovsky.
Optimization on a Grassmann manifold with application to system
identification.
Automatica, 50:1656-1662, 2014.
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[13]
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I. Markovsky, J. Goos, K. Usevich, and R. Pintelon.
Realization and identification of autonomous linear periodically
time-varying systems.
Automatica, 50:1632-1640, 2014.
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[14]
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M. Ishteva, K. Usevich, and I. Markovsky.
Factorization approach to structured low-rank approximation with
applications.
SIAM J. Matrix Anal. Appl., 35(3):1180-1204, 2014.
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[15]
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S. Rhode, K. Usevich, I. Markovsky, and F. Gauterin.
A recursive restricted total least-squares algorithm.
IEEE Trans. Signal Process., 62(21):5652-5662, 2014.
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[16]
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S. De Marchi and K. Usevich.
On certain multivariate Vandermonde determinants whose variables
separate.
Linear Algebra and Its Applications, 449:17-27, 2014.
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[17]
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R. Kannan, M. Ishteva, and H. Park.
Bounded matrix factorization for recommender system.
Knowledge and Information Systems, 39(3):491-511, 2014.
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[18]
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I. Markovsky and K. Usevich.
Structured low-rank approximation with missing data.
SIAM J. Matrix Anal. Appl., 34(2):814-830, 2013.
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[19]
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I. Markovsky.
A software package for system identification in the behavioral
setting.
Control Engineering Practice, 21(10):1422-1436, 2013.
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[20]
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M. Ishteva, P.-A. Absil, and P. Van Dooren.
Jacobi algorithm for the best low multilinear rank approximation of
symmetric tensors.
SIAM J. Matrix Anal. Appl., 34(2):651-672, 2013.
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[21]
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F. Le, I. Markovsky, C. Freeman, and E. Rogers.
Recursive identification of Hammerstein systems with application to
electrically stimulated muscle.
Control Engineering Practice, 20(4):386-396, 2012.
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[22]
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I. Markovsky.
On the complex least squares problem with constrained phase.
SIAM J. Matrix Anal. Appl., 32(3):987-992, 2011.
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Conference papers
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[1]
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P. Dreesen, M. Ishteva, and J. Schoukens.
Recovering Wiener-Hammerstein nonlinear state-space models using
linear algebra.
In In Proc. of the 17th IFAC Symposium on System Identification
(SYSID 2015), Beijing, China, 2015.
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[2]
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P. Dreesen, M. Ishteva, and J. Schoukens.
On the full and block-decoupling of nonlinear functions.
In PAMM-Proceedings of Applied Mathematics and Mechanics,
volume 15, pages 739-742, 2015.
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[3]
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I. Markovsky and R. Pintelon.
Consistent estimation of autonomous linear time-invariant systems
from multiple experiments.
In In the Proc. of the Conference on Noise and Vibration
Engineering (ISMA), pages 3265-3268, Leuven, Belgium, September 2014.
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[4]
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M. Ishteva and I. Markovsky.
Tensor low multilinear rank approximation by structured matrix
low-rank approximation.
In In the Proc. of the 21st International Symposium on
Mathematical Theory of Networks and Systems (MTNS 2014), pages 1808-1812,
Groningen, The Netherlands, July 2014.
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[5]
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K. Usevich.
Decomposing multivariate polynomials with structured low-rank matrix
completion.
In Proc. of the 21th International Symposium on Mathematical
Theory of Networks and Systems, pages 1826-1833, 2014.
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[6]
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A. Van Mulders, L. Vanbeylen, and K. Usevich.
Identification of a block-structured model with several sources of
nonlinearity.
In Proc. of the 14th European Control Conference, pages
1717-1722, 2014.
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[7]
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I. Markovsky.
Approximate identification with missing data.
In In the Proc. of the 52nd IEEE Conference on Decision and
Control, pages 156-161, Florence, Italy, December 2013.
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[8]
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I. Markovsky.
Exact identification with missing data.
In In the Proc. of the 52nd IEEE Conference on Decision and
Control, pages 151-155, Florence, Italy, 2013.
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[9]
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M. Ishteva, L. Song, and H. Park.
Unfolding latent tree structures using 4th order tensors.
In In Proc. of the International Conference on Machine Learning
(ICML), 2013.
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[10]
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L. Song, M. Ishteva, A. Parikh, E. Xing, and H. Park.
Hierarchical tensor decomposition of latent tree graphical models.
In International Conference on Machine Learning (ICML), 2013.
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[11]
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K. Usevich.
Improved initial approximation for errors-in-variables system
identification.
In Proc. of the 20th Mediterranean Conference on Control and
Automation, pages 198-203, Barcelona, Spain, 2012, July 2012.
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[12]
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I. Markovsky.
Dynamical systems and control mindstorms.
In In the Proc. of the 20th Mediterranean Conference on Control
and Automation, pages 54-59, Barcelona, Spain, 2012.
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[13]
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K. Usevich and I. Markovsky.
Structured low-rank approximation as a rational function
minimization.
In In the Proc. of the 16th IFAC Symposium on System
Identification, pages 722-727, Brussels, 2012.
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[14]
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I. Markovsky.
How effective is the nuclear norm heuristic in solving data
approximation problems?
In In the Proc. of the 16th IFAC Symposium on System
Identification, pages 316-321, Brussels, 2012.
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Book chapters
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[1]
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R. Kannan, M. Ishteva, B. Drake, and H. Park.
Bounded matrix low rank approximation.
In G. R. Naik, editor, Non-negative Matrix Factorization
Techniques, Signals and Communication Technology, pages 89-118. Springer
Berlin Heidelberg, 2016.
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[2]
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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.
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[3]
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M. Ishteva.
Tensors and latent variable models.
In E. Vincent, A. Yeredor, Z. Koldovský, and P. Tichavský,
editors, Latent Variable Analysis and Signal Separation, volume 9237 of
Lecture Notes in Computer Science, pages 49-55. Springer International
Publishing, 2015.
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[4]
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P. Dreesen, T. Goossens, M. Ishteva, L. De Lathauwer, and J. Schoukens.
Block-decoupling multivariate polynomials using the tensor block-term
decomposition.
In E. Vincent, A. Yeredor, Z. Koldovský, and P. Tichavský,
editors, Latent Variable Analysis and Signal Separation, volume 9237 of
Lecture Notes in Computer Science, pages 14-21. Springer International
Publishing, 2015.
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[5]
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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.
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[6]
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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.
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[7]
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I. Markovsky.
Algorithms and literate programs for weighted low-rank approximation
with missing data.
volume 3, chapter 12, pages 255-273. Springer, 2011.
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Technical reports
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[1]
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I. Markovsky and G. Mercére.
Subspace identification with constraints on the impulse response.
Technical report, Vrije Univ. Brussel, 2016.
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[2]
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I. Markovsky, Otto Debals, and Lieven De Lathauwer.
Sum-of-exponentials modeling and common dynamics estimation using
tensorlab.
Technical report, Vrije Univ. Brussel, 2015.
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[3]
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I. Markovsky.
A low-rank matrix completion approach to data-driven signal
processing.
Technical report, Vrije Univ. Brussel, 2015.
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[4]
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N. Guglielmi and I. Markovsky.
Computing the distance to uncontrollability: the SISO case.
Technical report, Vrije Univ. Brussel, 2014.
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