MIRON , Sebastian

Dans cet article, nous proposons une approche générique pour les décompositions booléennes de matrices de données binaires. Dans notre approche, les matrices binaires de rang 1 du modèle de factorisation peuvent être combinées par une fonction booléenne arbitraire, généralisant ainsi le modèle standard de factorisation booléenne, où la...

We introduce a versatile approach for Boolean factorization of binary data matrices based on a projected hierarchical alternating least squares method. The general model considered in this work allows for an arbitrary Boolean combination of the binary rank-1 terms. The underlying approximation problem is tackled by relaxing the binary...

In this paper, we propose a generalized framework for fitting Boolean matrix factorization models to binary data. In this generalized setting, the binary rank-1 components of the underlying model can be combined by any Boolean function, thus extending the standard Boolean matrix factorization model, where the combination is restricted to the...

Over the last two decades, tensor-based methods have received growing attention in the signal processing community. In this work, we propose a comprehensive overview of tensor-based models and methods for multisensor signal processing. We present for instance the Tucker decomposition, the Canonical Polyadic Decomposition (CPD), the Tensor-Train...