247 citations · 609 across the 27 of their papers we have counts for
30 papers
Joint Normality Test Via Two-Dimensional Projection
Sara Elbouch, Olivier Michel, Pierre Comon
Extensive literature exists on how to test for normality, especially for identically and independently distributed (i.i.d) processes. The case of dependent samples has also been ad…
A Normality Test for Multivariate Dependent Samples
Sara Elbouch, Olivier Michel, Pierre Comon
Most normality tests in the literature are performed for scalar and independent samples. Thus, they become unreliable when applied to colored processes, hampering their use in real…
A Random Matrix Perspective on Random Tensors
José Henrique de Morais Goulart, Romain Couillet, Pierre Comon
Tensor models play an increasingly prominent role in many fields, notably in machine learning. In several applications, such as community detection, topic modeling and Gaussian mix…
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…
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…