54 citations · 128 across the 18 of their papers we have counts for
5 papers · 1 filter
Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data
Giovanni Seraghiti, Kévin Dubrulle, Arnaud Vandaele +1
Nonnegative matrix factorization (NMF) approximates a nonnegative matrix, X, by the product of two nonnegative factors, WH, where W has r columns and H has r rows. In this paper, w…
Efficient algorithms for the Hadamard decomposition
Samuel Wertz, Arnaud Vandaele, Nicolas Gillis
The Hadamard decomposition is a powerful technique for data analysis and matrix compression, which decomposes a given matrix into the element-wise product of two or more low-rank m…
Accelerated Algorithms for Nonlinear Matrix Decomposition with the ReLU function
Giovanni Seraghiti, Atharva Awari, Arnaud Vandaele +2
In this paper, we study the following nonlinear matrix decomposition (NMD) problem: given a sparse nonnegative matrix , find a low-rank matrix such that , wh…
Matrix-wise -constrained Sparse Nonnegative Least Squares
Nicolas Nadisic, Jeremy E Cohen, Arnaud Vandaele +1
Nonnegative least squares problems with multiple right-hand sides (MNNLS) arise in models that rely on additive linear combinations. In particular, they are at the core of most non…
Sparse Separable Nonnegative Matrix Factorization
Nicolas Nadisic, Arnaud Vandaele, Jeremy E. Cohen +1
We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions. Separability requires that the columns of the first NMF factor…