.png){kind=icon}
返回
A survey on deep matrix factorizations
.png){kind=icon}
OA
AI
DOI：10.1016/j.cosrev.2021.100423.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. (C) 2021 Elsevier Inc. All rights reserved.
Keyword:
Machine learning
Matrix factorizations
Deep learning
Data mining
Unsupervised learning
期刊
IF:
12.7
论文数:
2.3K
被引数:
5.2K
机构
引用论文
Revisiting the effectiveness of economic sanctions in the context of Russia’s invasion of Ukraine
PREAI
分享收藏MULTILAYER TENSOR FACTORIZATION WITH APPLICATIONS TO RECOMMENDER SYSTEMS.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏
查看更多内容