Manifold regularized stacked denoising autoencoders with feature selection

摘要

This paper proposes a new stacked denoising autoencoders (SDAE), called manifold regularized SDAE (MRSDAE) based on particle swarm optimization (PSO), where manifold regularization and feature selection are embedded in the deep network. This study concentrates on using PSO to simultaneously optimize structure and parameters of SDAEs through a specific particle representation and learning method. MRSDAE aims to generate discriminant features from the data based on the integration of these effective techniques, i.e., structure and parameter optimization, manifold regularization and feature selection. The experimental results on a number of benchmark classification datasets demonstrate that MRSDAE can construct compact SDAEs with high generalization performance. Finding from this study can be used as effective guideline in learning both the structure and parameters of deep neural networks (DNNs) with manifold regularization and feature selection techniques. (C) 2019 Elsevier B.V. All rights reserved.