92 citations · 337 across the 28 of their papers we have counts for
33 papers
Globally Convergent Jacobi-Type Algorithms for Robust Approximate Diagonalization of Higher-Order Cumulant Tensors
Jianze Li, Zhenyu Cai, Wentao Ding +1
The sums of the absolute values of higher-order cumulants are classical contrast functions for independent component analysis (ICA) and lead naturally to robust approximate diagona…
On factorization of rank-one auto-correlation matrix polynomials
Konstantin Usevich, Julien Flamant, Marianne Clausel +1
This article characterizes the rank-one factorization of auto-correlation matrix polynomials. We establish a sufficient and necessary uniqueness condition for uniqueness of the fac…
A lifting approach to ParaTuck-2 tensor decompositions
Konstantin Usevich
The ParaTuck-2 decomposition (PT2D) of third-order tensor is a two-layer generalization of the well-known canonical polyadic decomposition (CPD).While being more flexible than the…
Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion
Ricardo Augusto Borsoi, Isabell Lehmann, Mohammad Abu Baker Siddique Akhonda +4
Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tas…
Polarimetric phase retrieval: uniqueness and algorithms
Julien Flamant, Konstantin Usevich, Marianne Clausel +1
This work introduces a novel Fourier phase retrieval model, called polarimetric phase retrieval that enables a systematic use of polarization information in Fourier phase retrieval…
Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review
Jonathan Gillard, Konstantin Usevich
In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time seri…
Gaussian Process Regression in the Flat Limit
Simon Barthelmé, Pierre-Olivier Amblard, Nicolas Tremblay +1
Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regressio…
Robust Eigenvectors of Symmetric Tensors
Tommi Muller, Elina Robeva, Konstantin Usevich
The tensor power method generalizes the matrix power method to higher order arrays, or tensors. Like in the matrix case, the fixed points of the tensor power method are the eigenve…
Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems
Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decomposi…
Determinantal Point Processes in the Flat Limit
Simon Barthelmé, Nicolas Tremblay, Konstantin Usevich +1
Determinantal point processes (DPPs) are repulsive point processes where the interaction between points depends on the determinant of a positive-semi definite matrix. In this paper…
Extended L-ensembles: a new representation for Determinantal Point Processes
Nicolas Tremblay, Simon Barthelmé, Konstantin Usevich +1
Determinantal point processes (DPPs) are a class of repulsive point processes, popular for their relative simplicity. They are traditionally defined via their marginal distribution…
Tensor-based framework for training flexible neural networks
Yassine Zniyed, Konstantin Usevich, Sebastian Miron +1
Activation functions (AFs) are an important part of the design of neural networks (NNs), and their choice plays a predominant role in the performance of a NN. In this work, we are…
Convergence of gradient-based block coordinate descent algorithms for non-orthogonal joint approximate diagonalization of matrices
Jianze Li, Konstantin Usevich, Pierre Comon
In this paper, we propose a gradient-based block coordinate descent (BCD-G) framework to solve the joint approximate diagonalization of matrices defined on the product of the compl…
Determinantal Point Processes in the Flat Limit: Extended L-ensembles, Partial-Projection DPPs and Universality Classes
Simon Barthelmé, Nicolas Tremblay, Konstantin Usevich +1
Determinantal point processes (DPPs) are repulsive point processes where the interaction between points depends on the determinant of a positive-semi definite matrix. The contribut…
Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability
Ricardo Augusto Borsoi, Clémence Prévost, Konstantin Usevich +3
Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with st…
On the convergence of Jacobi-type algorithms for Independent Component Analysis
Jianze Li, Konstantin Usevich, Pierre Comon
Jacobi-type algorithms for simultaneous approximate diagonalization of real (or complex) symmetric tensors have been widely used in independent component analysis (ICA) because of…
Jacobi-type algorithm for low rank orthogonal approximation of symmetric tensors and its convergence analysis
Jianze Li, Konstantin Usevich, Pierre Comon
In this paper, we propose a Jacobi-type algorithm to solve the low rank orthogonal approximation problem of symmetric tensors. This algorithm includes as a special case the well-kn…
Spectral properties of kernel matrices in the flat limit
Simon Barthelmé, Konstantin Usevich
Kernel matrices are of central importance to many applied fields. In this manuscript, we focus on spectral properties of kernel matrices in the so-called ``flat limit'', which occu…
Approximate matrix and tensor diagonalization by unitary transformations: convergence of Jacobi-type algorithms
Konstantin Usevich, Jianze Li, Pierre Comon
We propose a gradient-based Jacobi algorithm for a class of maximization problems on the unitary group, with a focus on approximate diagonalization of complex matrices and tensors…
Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithms
Clémence Prévost, Konstantin Usevich, Pierre Comon +1
We propose a novel approach for hyperspectral super-resolution, that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the c…
On approximate diagonalization of third order symmetric tensors by orthogonal transformations
Jianze Li, Konstantin Usevich, Pierre Comon
In this paper, we study the approximate orthogonal diagonalization problem of third order symmetric tensors. We define several classes of approximately diagonal tensors, including…
Structured low-rank matrix completion for forecasting in time series analysis
Jonathan Gillard, Konstantin Usevich
In this paper we consider the low-rank matrix completion problem with specific application to forecasting in time series analysis. Briefly, the low-rank matrix completion problem i…
Decoupling multivariate polynomials: interconnections between tensorizations
Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
Decoupling multivariate polynomials is useful for obtaining an insight into the workings of a nonlinear mapping, performing parameter reduction, or approximating nonlinear function…
Globally convergent Jacobi-type algorithms for simultaneous orthogonal symmetric tensor diagonalization
Jianze Li, Konstantin Usevich, Pierre Comon
In this paper, we consider a family of Jacobi-type algorithms for simultaneous orthogonal diagonalization problem of symmetric tensors. For the Jacobi-based algorithm of [SIAM J. M…