CORDIS Project
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The project explores generalized low-rank matrix factorizations (G-LRMFs) to enhance data analysis by overcoming limitations of traditional linear models. It focuses on developing theoretical foundations, algorithms, and tailored models for various applications in machine learning and data processing.
Low-rank matrix factorizations (LRMFs), such as principal component analysis, nonnegative matrix factorization and sparse component analysis, are linear dimensionality reduction techniques and are powerful unsupervised models to represent and analyze high-dimensional data sets.
They are used in a wide variety of areas such as machine learning, signal processing, and data mining.
Many LRMFs have been proposed in the literature, in particular in the last two decades, and used extensively in many a…
UNIVERSITE DE MONS
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