Fractal Autoencoders for Feature Selection. (arXiv:2010.09430v2 [cs.LG] UPDATED)

Feature selection reduces the dimensionality of data by identifying a subset
of the most informative features. In this paper, we propose an innovative
framework for unsupervised feature selection, called fractal autoencoders
(FAE). It trains a neural network to pinpoint informative features for global
exploring of representability and for local excavating of diversity.
Architecturally, FAE extends autoencoders by adding a one-to-one scoring layer
and a small sub-neural network for feature selection in an unsupervised
fashion. With such a concise architecture, FAE …

arxiv feature selection