ERC Consolidator Grant · 2022
Beyond Low-Rank Factorizations
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 applications, such as recommender systems, blind source separation, and text mining. Although LRMFs have known and still know tremendous success, they have several limitations. Two key limitations are…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.