2 citations · 2 across the 7 of their papers we have counts for
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The ReLU Matrix Decomposition: NP-Hardness, Ill-Posedness, and ReLU-Rank
Giovanni Seraghiti, Arnaud Vandaele, Nicolas Gillis
The ReLU Matrix Decomposition (RMD) approximates a sparse, nonnegative m-by-n matrix X by max(0, WH), where W is an m-by-r matrix, H is an r-by-n matrix, and r is the rank of the d…
Nonlinear Tensor Decomposition for Pattern Discovery
Atharva Awari, Geert Roelof van der Ploeg, Arnaud Vandaele +2
The CANDECOMP/PARAFAC (CP) decomposition is widely used for revealing the underlying patterns from multiway data (also referred to as a higher-order tensor). When the data is clipp…
Degree-Corrected Joint Matrix Factorization for Multilayer Community Detection
Alexandra Dache, Manon Rustin, Arnaud Vandaele +1
Multilayer networks allow the modeling of interactions between the same entities across different contexts, such as temporal observations, varying settings, or interactions of diff…
Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis
Amjad Seyedi, Lifang He, Songlin Zhao +2
Multimodal brain network analysis faces a persistent trade-off between predictive accuracy and interpretability. Deep neural networks achieve high accuracy but behave as black boxe…
Computing Lower Bounds on the Nonnegative Rank via Non-Convex Optimization Solvers
Timothy Baeckelant, Arnaud Vandaele, Nicolas Gillis
The nonnegative rank of a nonnegative matrix is the smallest number of nonnegative rank-one factors that sum to . Since computing the nonnegative rank is NP-hard, it is comm…
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…
Matrix Factorization Framework for Community Detection under the Degree-Corrected Block Model
Alexandra Dache, Arnaud Vandaele, Nicolas Gillis
Community detection is a fundamental task in data analysis, and block models provide an approach for identifying a wide variety of community structures while offering high interpre…