# Representation Learning: A Statistical Perspective

arXiv: https://arxiv.org/abs/1911.11374 · HTML: https://mlsanity.com/paper/1911.11374/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

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- [PEARL: Performance-Enhanced Aggregated Representation Learning](https://mlsanity.com/paper/2509.24312/) (2025)
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## Abstract (arXiv)

Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and multidimensional scaling in statistics, it has become a central theme in deep learning with important applications in computer vision and computational neuroscience. In this article, we review recent advances in learning representations from a statistical perspective. In particular, we review the following two themes: (a) unsupervised learning of vector representations and (b) learning of both vector and matrix representations.