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 clipped or saturated, however, the linear CP model becomes unreliable. Several remedies exist such as treating the clipped entries as missing or...
Atharva Awari, G. R. van der Ploeg, Arnaud Vandaele et al.· 0 citations
Multilayer networks allow the modeling of interactions between the same entities across different contexts, such as temporal observations, varying settings, or interactions of different types. The goal of community detection in multilayer networks is to identify groups of nodes exhibiting similar connectivity patterns,...
Alexandra Dache, Manon Rustin, Arnaud Vandaele et al.· 0 citations
Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes Symmetric Nonnegative Matrix Tri-Factorization from unsupervised single-graph clustering to supervised prediction over populations of multimodal graphs, is presented.
This paper considers NMF using the component-wise L1 norm as the error measure (L1-NMF), which is suited for data corrupted by heavy-tailed noise, such as Laplace noise or salt and pepper noise, or in the presence of outliers, and proposes a new coordinate descent approach for wL1-NMF, denoted as sparse CD (sCD), where...
Giovanni Seraghiti, K'evin Dubrulle, Arnaud Vandaele et al.· arXiv.org· 1 citation
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