Hierarchically sparse signals and Kronecker product structured measurements arise naturally in a variety of applications. The simplest example of a hierarchical sparsity structure is two-level (s, σ)-hierarchical sparsity which features s-block-sparse signals with σ-sparse blocks. For a large class of algorithms recovery guarantees can be derived based on the restricted isometry property (RIP) ...

Cointegrated VARMA models can be parameterized by using the echelon form, which is characterized by the Kronecker indices. Three diierent methods for estimating the Kronecker indices of cointegrated echelon form VARMA models are discussed and compared. They have the common feature of estimating the individual equations of the system separately and using order selection criteria. The small sampl...

In this paper, we exhibit the tradeoffs between (training) sample, computation and storage complexity for the problem of supervised classification using signal subspace estimation. Our main tool is the use of tensor subspaces, i.e. subspaces with a Kronecker structure, for embedding the data into lower dimensions. Among the subspaces with a Kronecker structure, we show that using subspaces with...

We propose a new approach to study the Kronecker coefficients by using the Schur–Weyl duality between the symmetric group and the partition algebra. Résumé. Nous proposons une nouvelle approche pour l’étude des coéfficients de Kronecker via la dualité entre le groupe symétrique et l’algèbre des partitions.

Truncated Singular Value Decomposition (TSVD) regularization method have been used by Zhao et al. [ " Kronecker product approximations for image restoration with new mean boundary conditions " (2011), Applied Mathematical Modelling, Vol. 36, pp. 225-237]. In this report, I propose an alternative regularization the Tikhonov method. The new regularization method gives better relative error when a...