Author

Giovanni Seraghiti

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#machine learning Mar 2026

Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data

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