Deep matrix factorizations
Pierre De Handschutter, Nicolas Gillis, Xavier Siebert

TL;DR
Deep matrix factorization extends traditional low-rank approximation techniques by enabling hierarchical feature extraction, inspired by deep learning, and has demonstrated outstanding performance in unsupervised tasks.
Contribution
This paper provides a comprehensive review of deep matrix factorization models, algorithms, applications, and discusses future research directions.
Findings
Deep MF achieves superior performance on unsupervised tasks.
It effectively extracts hierarchical features from complex data.
The paper discusses theoretical aspects and potential research perspectives.
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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Taxonomy
TopicsTensor decomposition and applications · Sparse and Compressive Sensing Techniques · Face and Expression Recognition
