5 accepted papers
We propose a novel approach to solve exactly the sparse nonnegative least squares problem, under hard ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> sparsity constraints. This approach is based on a dedicated branch-and-bound algorithm. This…
Tensor decompositions have become a central tool in machine learning to extract interpretable patterns from multiway arrays of data. However, computing the approximate Canonical Polyadic Decomposition (aCPD), one of the most important tensor decomposition model, remains a challenge. In this work, we…
In this paper we consider a variant of the dictionary learning problem where the dictionary has full rank, the coefficients have a fixed sparsity level, and both the coefficients and the dictionary are nonnegative. It is equivalent to k-sparse nonnegative matrix factorization (K-NMF). This model is…
In this paper, we show that tensor compression techniques based on randomization and partial observations are very useful for spatial audio object coding. In this application, we aim at transmitting several audio signals called objects from a coder to a decoder. A common strategy is to transmit only…
The Canonical Polyadic tensor decomposition (CPD), also known as Candecomp/Parafac, is very useful in numerous scientific disciplines. Structured CPDs, i.e. with Toeplitz, circulant, or Hankel factor matrices, are often encountered in signal processing applications. As subsequently pointed out, spec…