2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
Checking the Sufficiently Scattered Condition using a Global Non-Convex Optimization Software
Nicolas Gillis, Robert Luce
The sufficiently scattered condition (SSC) is a key condition in the study of identifiability of various matrix factorization problems, including nonnegative, minimum-volume, symme…