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
TypeCommunication / Conférence
External document linkhttps://hal-normandie-univ.archives-ouvertes.fr/hal-02895832
Conference title28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020)
MetadataShow full item record
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 / KeywordsGraph space
Showing items related by title and author.
Lotte, Fabien; Bougrain, Laurent; Cichocki, Andrzej; Clerc, Maureen; Congedo, Marco; Rakotomamonjy, Alain; Yger, Florian (2018) Article accepté pour publication ou publié