Deep matrix factorizations
It addresses the limitation of traditional methods in mining complex, interleaved features for unsupervised learning, though it is incremental as a review and extension of existing concepts.
The paper reviews deep matrix factorization (deep MF) as an extension of low-rank matrix approximations to extract hierarchical features, achieving outstanding performance on unsupervised tasks.
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.