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Representation Learning: A Statistical Perspective
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DOI：10.1146/annurev-statistics-031219-041131.png)
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Abstract
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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.
Keywords:
unsupervised learning
generative representations
relative representations
predictive representations
vector representations
matrix representations
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