Comparing linear structure-based and data-driven latent spatial representations for sequence prediction

Myriam Bontonou; Carlos Lassance; Vincent Gripon; Nicolas Farrugia

doi:10.1117/12.2528450

9 September 2019 Comparing linear structure-based and data-driven latent spatial representations for sequence prediction

Myriam Bontonou, Carlos Lassance, Vincent Gripon, Nicolas Farrugia

Author Affiliations +

Proceedings Volume 11138, Wavelets and Sparsity XVIII; 111380Z (2019) https://doi.org/10.1117/12.2528450
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

Predicting the future of Graph-supported Time Series (GTS) is a key challenge in many domains, such as climate monitoring, finance or neuroimaging. Yet it is a highly difficult problem as it requires to account jointly for time and graph (spatial) dependencies. To simplify this process, it is common to use a two-step procedure in which spatial and time dependencies are dealt with separately. In this paper, we are interested in comparing various linear spatial representations, namely structure-based ones and data-driven ones, in terms of how they help predict the future of GTS. To that end, we perform experiments with various datasets including spontaneous brain activity and raw videos.

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Myriam Bontonou, Carlos Lassance, Vincent Gripon, and Nicolas Farrugia "Comparing linear structure-based and data-driven latent spatial representations for sequence prediction", Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111380Z (9 September 2019); https://doi.org/10.1117/12.2528450

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