Deep Dimension Reduction for Supervised Representation Learning. (arXiv:2006.05865v3 [cs.LG] UPDATED)

The goal of supervised representation learning is to construct effective data
representations for prediction. Among all the characteristics of an ideal
nonparametric representation of high-dimensional complex data, sufficiency, low
dimensionality and disentanglement are some of the most essential ones. We
propose a deep dimension reduction approach to learning representations with
these characteristics. The proposed approach is a nonparametric generalization
of the sufficient dimension reduction method. We formulate the ideal
representation learning task as that of finding a nonparametric representation
that …

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