CORDIS Project
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This project investigates low-rank matrix approximation techniques to improve data representation in various fields. It aims to bridge theoretical gaps by developing new algorithms and tools, enabling practitioners to better analyze high-dimensional data.
Low-rank matrix approximation (LRA) techniques such as principal component analysis (PCA) are powerful tools for the representation and analysis of high dimensional data, and are used in a wide variety of areas such as machine learning, signal and image processing, data mining, and optimization.
Without any constraints and using the least squares error, LRA can be solved via the singular value decomposition.
However, in practice, this model is often not suitable mainly because (i) the data might…
UNIVERSITE DE MONS
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