Estimation of the Sample Frechet Mean: A Convolutional Neural Network Approach

2022/10/13 by Adam Sanchez, Sanchez, Adam, François G. Meyer +1
Computer Science · Physics and Astronomy · #Bayesian Modeling and Causal Inference #Complex Network Analysis Techniques #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.2210.07401

openalex publication_date 2022/10/13 · openalex created_date 2022/10/19 · openalex updated_date 2026/07/28

Abstract

This work addresses the rising demand for novel tools in statistical and machine learning for "graph-valued random variables" by proposing a fast algorithm to compute the sample Frechet mean, which replaces the concept of sample mean for graphs (or networks). We use convolutional neural networks to learn the morphology of the graphs in a set of graphs. Our experiments on several ensembles of random graphs demonstrate that our method can reliably recover the sample Frechet mean.

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