A survey on deep matrix factorizations
| Autor: | Nicolas Gillis, Xavier Siebert, Pierre De Handschutter |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
General Computer Science
Artificial neural network Computer science business.industry Dimensionality reduction Deep learning 020206 networking & telecommunications 02 engineering and technology Semantics Machine learning computer.software_genre Theoretical Computer Science Matrix decomposition Matrix (mathematics) 0202 electrical engineering electronic engineering information engineering Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Computer Science Review. 42:100423 |
| ISSN: | 1574-0137 |
| DOI: | 10.1016/j.cosrev.2021.100423 |
| Popis: | Constrained low-rank matrix approximations have been known for decades as powerful linear dimensionality reduction techniques 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 survey 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 as deep MF is likely to become an important paradigm in unsupervised learning in the next few years. |
| Databáze: | OpenAIRE |
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