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Fréchet Mean Computation in Graph Space through Projected Block Gradient Descent

Boria, Nicolas; Negrevergne, Benjamin; Yger, Florian (2020), Fréchet Mean Computation in Graph Space through Projected Block Gradient Descent, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020), 2020-10, Bruges, FRANCE

Type
Communication / Conférence
External document link
https://hal-normandie-univ.archives-ouvertes.fr/hal-02895832
Date
2020
Conference title
28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020)
Conference date
2020-10
Conference city
Bruges
Conference country
FRANCE
Metadata
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Author(s)
Boria, Nicolas cc
Negrevergne, Benjamin cc
Yger, Florian cc
Abstract (EN)
A fundamental concept in statistics is the concept of Fréchet sample mean. While its computation is a simple task in Euclidian space, the same does not hold for less structured spaces such as the space of graphs, where concepts of distance or mid-point can be hard to compute. We present some work in progress regarding new distance measures and new algorithms to compute the Fréchet mean in the space of Graphs.
Subjects / Keywords
Graph space