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arXiv · 2010.00380

Deep matrix factorizations

Pierre De Handschutter·Nicolas Gillis·Xavier Siebert
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Abstract

Constrained low-rank matrix approximations have been known for decades as powerful linear dimensionality reduction techniques to be able to extract the information contained in large data sets in a relevant way. However, such low-rank approaches are unable to mine complex, interleaved features that underlie hierarchical semantics. Recently, deep matrix factorization (deep MF) was introduced to deal with the extraction of several layers of features and has been shown to reach outstanding performances on unsupervised tasks. Deep MF was motivated by the success of deep learning, as it is conceptually close to some neural networks paradigms. In this paper, we present the main models, algorithms, and applications of deep MF through a comprehensive literature review. We also discuss theoretical questions and perspectives of research.

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BibTeXRIS

Pierre De Handschutter, Nicolas Gillis, Xavier Siebert. 2020-10-01. Deep matrix factorizations. https://doi.org/10.1016/j.cosrev.2021.100423

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