Graph Signal Recovery Using Restricted Boltzmann Machines

• URL: http://arxiv.org/abs/2011.10549v1
• Date: Fri, 20 Nov 2020 18:43:53 GMT
• Title: Graph Signal Recovery Using Restricted Boltzmann Machines
• Authors: Ankith Mohan, Aiichiro Nakano, Emilio Ferrara
• Abstract summary: We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine. We show that denoising the representations learned by the deep neural networks is usually more effective than denoising the data itself.
• Score: 11.077860020575084
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine and the representational ability of a neural network. The proposed pipeline requires the deep neural network that is trained on a downward machine learning task with clean data, data which is free from any form of corruption or incompletion. We show that denoising the representations learned by the deep neural networks is usually more effective than denoising the data itself. Although this pipeline can deal with noise in any dataset, it is particularly effective for graph-structured datasets.

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