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Deep Dimension Reduction for Supervised Representation Learning. (arXiv:2006.05865v3 [cs.LG] UPDATED)
Sept. 2, 2022, 1:12 a.m. | Jian Huang, Yuling Jiao, Xu Liao, Jin Liu, Zhou Yu
cs.LG updates on arXiv.org arxiv.org
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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