Representation Learning: A Statistical Perspective [article]

Jianwen Xie, Ruiqi Gao, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu
2019 arXiv   pre-print
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:
more » ... (a) unsupervised learning of vector representations and (b) learning of both vector and matrix representations.
arXiv:1911.11374v1 fatcat:uo47fvw4xndnhm35kr2vjrolpi