2 research outputs found
Deep Dimension Reduction for Supervised Representation Learning
The success of deep supervised learning depends on its automatic data
representation abilities. Among all the characteristics of an ideal
representation for high-dimensional complex data, information preservation, low
dimensionality and disentanglement are the most essential ones. In this work,
we propose a deep dimension reduction (DDR) approach to achieving a good data
representation with these characteristics for supervised learning. At the
population level, we formulate the ideal representation learning task as
finding a nonlinear dimension reduction map that minimizes the sum of losses
characterizing conditional independence and disentanglement. We estimate the
target map at the sample level nonparametrically with deep neural networks. We
derive a bound on the excess risk of the deep nonparametric estimator. The
proposed method is validated via comprehensive numerical experiments and real
data analysis in the context of regression and classification