Publications

Submitted

  • T. Muller, E. Robeva, K. Usevich (2021). Robust Eigenvectors of Symmetric Tensors. [arXiv preprint]
  • Y. Zniyed, K. Usevich, S. Miron, D. Brie (2021). Tensor-based framework for training flexible neural networks. [arXiv preprint]
  • J. Li, K. Usevich, P. Comon (2021). Gradient based block coordinate descent algorithms for joint approximate diagonalization of matrices. [arXiv preprint]
  • S. Barthelmé, N. Tremblay, K. Usevich, P.-O. Amblard (2020). Determinantal Point Processes in the Flat Limit: Extended L-ensembles, Partial-Projection DPPs and Universality Classes. [arXiv preprint]
  • C. Prévost, K. Usevich, M. Haardt, P. Comon, and D. Brie (2020). Constrained Cramér-Rao lower bounds for CP-based hyperspectral super-resolution. [HAL preprint]

Journal papers

  • C. Prévost, R. Borsoi, K. Usevich, D. Brie, J. C. M. Bermudez, C. Richard (2021). Hyperspectral super-resolution accounting for spectral variability: coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image. SIAM Journal on Imaging Sciences. Accepted. [HAL preprint]
  • J. Li, K.Usevich, and P. Comon (2021). Jacobi-type algorithm for low rank orthogonal approximation of symmetric tensors and its convergence analysis. Pacific Journal of Optimization. 17(3):357-379.[arXiv preprint]
  • R. Borsoi, C. Prévost, K. Usevich, D. Brie, J. C. M. Bermudez, C. Richard (2021). Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Variability. IEEE Journal of Selected Topics and Signal Processing. 15(3):702-717. [arXiv preprint]
  • S. Barthelmé, K. Usevich (2021). Spectral properties of kernel matrices in the flat limit. SIAM Journal on Matrix Analysis and applications. 42(1):17-57. [arXiv preprint]
  • K. Usevich, J. Li, and P. Comon (2020). Approximate matrix and tensor diagonalization by unitary transformations: convergence of Jacobi-type algorithms. SIAM Journal on Optimization. 30(4):2998-3028. [arXiv preprint]
  • C. Prévost, K. Usevich, P. Comon, and D. Brie (2020). Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithms. IEEE Transactions on Signal Processing. 68:931-946. [doi:10.1109/TSP.2020.2965305] [HAL preprint]
  • K. Usevich, P. Dreesen, and M. Ishteva (2020). Decoupling multivariate polynomials: interconnections between tensorizations. Journal of Computational and Applied Mathematics. 363:22-34. [doi:10.1016/j.laa.2019.03.006] [arXiv preprint]
  • J.Li, K. Usevich, and P. Comon (2019). On approximate diagonalization of third order symmetric tensors by orthogonal transformations. Linear Algebra and its Applications. [doi:10.1016/j.laa.2019.03.006] [arXiv preprint]
  • J. Gillard, K. Usevich (2018). Structured low-rank matrix completion for forecasting in time series analysis. International Journal of Forecasting. 34(4):582-597. [doi:10.1016/j.ijforecast.2018.03.008] [arXiv preprint]
  • J. Li, K.Usevich and P. Comon (2018). Globally convergent Jacobi-type algorithms for simultaneous orthogonal symmetric tensor diagonalization. SIAM Journal on Matrix Analysis and applications. 39(1):1-22. [doi:10.1137/17M1116295] [arXiv preprint]
  • S. Sahnoun, K. Usevich and P. Comon (2017). Multidimensional ESPRIT for Damped and Undamped Signals: Algorithm, Computations and Perturbation Analysis. IEEE Transactions on Signal Processing. 65(22):5897-5910. [doi:10.1109/TSP.2017.2736512] [HAL preprint]
  • P. Comon, Y. Qi and K. Usevich (2017). Identifiability of an X-rank decomposition of polynomial maps. SIAM Journal on Applied Algebra and Geometry. 1(1):388-414. [doi:10.1137/16M1108388] [arXiv preprint]
  • K. Usevich and I. Markovsky (2017). Variable projection methods for approximate (greatest) common divisor computations. Theoretical Computer Science. 681:176-198. [doi:10.1016/j.tcs.2017.03.028] [arXiv preprint]
  • K. Usevich and P. Comon (2016). Hankel low-rank matrix completion: performance of the nuclear norm relaxation. IEEE Journal of Selected Topics and Signal Processing. [doi:10.1109/JSTSP.2016.2535182] [HAL preprint]
  • K. Usevich and I. Markovsky (2016). Adjusted least squares fitting of algebraic hypersurfaces. Linear Algebra and its Applications. 502:243-274. [doi:10.1016/j.laa.2015.07.023] [arXiv preprint]
  • N. Golyandina, A. Korobeynikov, A. Shlemov and K. Usevich (2015). Multivariate and 2D extensions of Singular Spectrum Analysis with the Rssa Package. Journal of Statistical Software. 67(2):1–78. [doi:10.18637/jss.v067.i02] [arXiv preprint].
  • M. Ishteva, K. Usevich and I. Markovsky (2014). Factorization Approach to Structured Low-Rank Approximation With Applications. SIAM Journal on Matrix Analysis and Applications. 35(3):1180–1204. [doi:10.1137/130931655] [arXiv preprint].
  • S. Rhode, K. Usevich, I. Markovsky and F. Gauterin (2014). A Recursive Restricted Total Least-squares Algorithm. IEEE Transactions on Signal Processing. 62(21):5652–5662. [doi:10.1109/TSP.2014.2350959] [PDF file]
  • S. De Marchi and K. Usevich (2014). On certain multivariate Vandermonde determinants whose variables separate. Linear Algebra and Its Applications. 449:17–27. [doi:10.1016/j.laa.2014.01.034] [PDF file]
  • K. Usevich and I. Markovsky (2014). Variable projection methods for affinely structured low-rank approximation in weighted $2$-norms. Journal of Computational and Applied Mathematics. 272:430–448. [doi:10.1016/j.cam.2013.04.034] [PDF file]
  • I. Markovsky and K. Usevich (2014). Software for weighted structured low-rank approximation. Journal of Computational and Applied Mathematics. 256:278–292. [doi:10.1016/j.cam.2013.07.048] [PDF file]
  • K. Usevich and I. Markovsky (2014). Optimization on a Grassmann manifold with application to system identification. Automatica. 50(6):1656–1662. [doi:10.1016/j.automatica.2014.04.010] [PDF file]
  • I. Markovsky, J. Goos, K. Usevich and R. Pintelon (2014). Realization and identification of autonomous linear periodically time-varying systems. Automatica. 50(6):1632–1640. Available from http://homepages.vub.ac.be/~imarkovs/publications.html. [doi:10.1016/j.automatica.2014.04.003]
  • I. Markovsky and K. Usevich (2013). Structured low-rank approximation with missing data. SIAM Journal on Matrix Analysis and Applications. 34(2):814–830. [doi:10.1137/120883050] [PDF file]
  • D. M. Holloway, F. J. P. Lopes, L. da Fontoura Costa, B. A. N. Travençolo, N. Golyandina, K. Usevich and A. V. Spirov (2011). Gene Expression Noise in Spatial Patterning: hunchback Promoter Structure Affects Noise Amplitude and Distribution in Drosophila Segmentation. PLoS Computational Biology. 7(2):e1001069. [doi:10.1371/journal.pcbi.1001069]
  • K. Usevich (2010). On signal and extraneous roots in Singular Spectrum Analysis. Statistics and Its Interface. 3(3):281–295. [doi:10.4310/SII.2010.v3.n3.a3] [PDF file]
  • N. Golyandina, I. Florinsky and K. Usevich (2007). Filtering of digital terrain models by 2D Singular Spectrum Analysis. International Journal of Ecology & Development. 8(F07):81–94. [PDF file]

Conference papers

  • K. Usevich, P. Dreesen, and M. Ishteva (2021). Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems.16th IFAC Symposium on System Identification, 13-16 July 2021, Padova, Italy. Accepted.
  • Y. Zniyed, K. Usevich, S. Miron, and D. Brie (2021). Learning nonlinearities in the decoupling problem with structured CPD.16th IFAC Symposium on System Identification, 13-16 July 2021, Padova, Italy. Accepted.
  • A. Fazzi,  N. Guglielmi, I. Markovsky, K. Usevich (2021). Common dynamic estimation via structured low-rank approximation with multiple rank constraints.16th IFAC Symposium on System Identification, 13-16 July 2021, Padova, Italy. Accepted.
  • J. Li,  K. Usevich, P. Comon (2020). On the Convergence of Jacobi-type Algorithms for Independent Component Analysis. 11th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 8-11 June 2020, Hangzhou, China. Accepted.
  • C. Prévost, E. Chaumette, K. Usevich, D. Brie, P. Comon (2020). On Cramér-Rao lower bounds with random equality constraints. 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020, Barcelona, Spain. [HAL preprint]
  • C. Prévost, K. Usevich, P. Comon, M. Haardt, D. Brie. Performance bounds for coupled CP model in the framework of hyperspectral super-resolution. 8th Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019, Dec 2019, Le Gosier, Guadeloupe, France. [HAL preprint]
  • K. Usevich, I. Markovsky (2019). Software package for mosaic-Hankel structured low-rank approximation. 58th IEEE Conference on Decision and Control, CDC 2019, Dec 2019, Nice, France, pages 7165-7170. [HAL preprint]
  • C. Prévost, K. Usevich, P. Comon, D. Brie (2019). Coupled Tensor Low-rank Multilinear Approximation for Hyperspectral Super-resolution. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 12-17 May, Brighton, UK. Accepted. [HAL preprint]
  • K. Usevich, V. Emiya, D. Brie, C. Chaux (2018). Characterization of finite signals with low-rank STFT. 2018 IEEE Statistical Signal Processing Workshop (SSP), 10-13 June 2018, Freiburg, Germany, pages 393-397. [doi:10.1109/SSP.2018.8450745] [HAL preprint]
  • K. Usevich, S. Sahnoun, and P. Comon (2017). High-resolution subspace-based methods: eigenvalue- or eigenvector-based estimation? In P. Tichavský, M. Babaie-Zadeh, O. Michel, and N. Thirion-Moreau, editors, Latent Variable Analysis and Signal Separation, vol. 10169 of LNCS, pp. 47–56. Springer. [doi:10.1007/978-3-319-53547-0_5] [HAL preprint]
  • S. Sahnoun, K. Usevich and P. Comon. (2016). Optimal choice of Hankel-block-Hankel matrix shape in 2-D parameter estimation. In 24th European Signal Processing Conference (EUSIPCO), 29 Aug.-2 Sept. 2016, Budapest, Hungary, pages 321-325. [doi:10.1109/EUSIPCO.2016.7760262] [HAL preprint]
  • M. Veganzones, J. Cohen, R. Cabral Farias, K. Usevich, L. Drumetz, J. Chanussot and P. Comon. (2016). Canonical polyadic decomposition of hyperspectral patch tensors. In 24th European Signal Processing Conference (EUSIPCO), 29 Aug.-2 Sept. 2016, Budapest, Hungary, pages 2176-2180. [doi:10.1109/EUSIPCO.2016.7760634] [HAL preprint]
  • P. Comon, Y. Qi and K. Usevich (2015). A polynomial formulation for joint decomposition of symmetric tensors of different orders. 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 22–30. Springer, 2015. [doi:10.1007/978-3-319-22482-4_3] [HAL preprint]
  • K. Usevich (2014). Decomposing multivariate polynomials with structured low-rank matrix completion. In 21st International Symposium on Mathematical Theory of Networks and Systems, July 7-11, 2014. Groningen, The Netherlands, pages 1826–1833. [PDF file]
  • A. Van Mulders, L. Vanbeylen and K. Usevich (2014). Identification of a block-structured model with several sources of nonlinearity. In Proceedings of the 14th European Control Conference (ECC 2014), pages 1717–1722. [doi:10.1109/ECC.2014.6862455] [PDF file]
  • N. E. Golyandina, D. M. Holloway, F. J. P. Lopes, A. V. Spirov, E. N. Spirova and K. D. Usevich (2012). Measuring gene expression noise in early Drosophila embryos: nucleus-to-nucleus variability. In Procedia Computer Science, pages 373–382. [doi:10.1016/j.procs.2012.04.040] [PDF file]
  • K. Usevich (2012). Improved initial approximation for errors-in-variables system identification. In Proceedings of 20th Mediterranean Conference on Control and Automation, pages 198–203. [doi:10.1109/MED.2012.6265638] [PDF file]
  • K. Usevich and I. Markovsky (2012). Structured low-rank approximation as a rational function minimization . In Proceedings of 16th IFAC Symposium on System Identification, pages 722–727. [doi:10.3182/20120711-3-BE-2027.00143] [PDF file]
  • N. Alexeyeva, A. Alexeyev, P. Gracheva, E. Podkhalyuzina and K. Usevich (2010). Symptom and syndrome analysis of categorial series, logical principles and forms of logic . In Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics (BMEI), pages 2603–2606. [doi:10.1109/BMEI.2010.5639574] [PDF file]
  • N. Golyandina and K. Usevich (2009). An algebraic view on finite rank in 2D-SSA. In Proceedings of the 6th St. Petersburg Workshop on Simulation, pages 308–313. [PDF file]

 

Book chapters

  • I. Markovsky and K. Usevich (2014). Nonlinearly structured low-rank approximation. In Yun Raymond Fu, editor, Low-Rank and Sparse Modeling for Visual Analysis, pages 1-22. Springer, 2014. [doi:10.1007/978-3-319-12000-3_1] Available from http://homepages.vub.ac.be/~imarkovs/publications.html.
  • N. Golyandina and K. Usevich (2010). 2D-extension of Singular Spectrum Analysis: algorithm and elements of theory. In V. Olshevsky and E. Tyrtyshnikov, editors, Matrix Methods: Theory, Algorithms and Applications, pages 449–474. [PDF file]

 

Technical reports

  • J. E. E. Cohen, K. Usevich, and P. Comon (2016). A Tour of Constrained Tensor Canonical Polyadic Decomposition. [HAL preprint].
  • K. Usevich (2011). Polynomial-exponential 2D data models, Hankel-block-Hankel matrices and zero-dimensional ideals. In International Conference on Polynomial Computer Algebra '2011, book of abstracts, pages 118–122. International Conference on Polynomial Computer Algebra '2011, book of abstracts. [PDF file]

 

Theses

  • K. Usevich (2011). Singular spectrum analysis for temporal and spatial data processing. (in Russian) [PDF file]