#6010. A survey on deep matrix factorizations

August 2026publication date
Proposal available till 08-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Theoretical Computer Science;
Computer Science (all);
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №6010.1 Contract №6010.2 Contract №6010.3 Contract №6010.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
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.
Keywords:
Data mining; Deep learning; Machine learning; Matrix factorizations; Unsupervised learning

Contacts :
0