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4 papers · 1 filter
Diffusion Maps meet Nyström
N. Benjamin Erichson, Lionel Mathelin, Steven L. Brunton +1
Diffusion maps are an emerging data-driven technique for non-linear dimensionality reduction, which are especially useful for the analysis of coherent structures and nonlinear embe…
Observable dictionary learning for high-dimensional statistical inference
Lionel Mathelin, Kévin Kasper, Hisham Abou-Kandil
This paper introduces a method for efficiently inferring a high-dimensional distributed quantity from a few observations. The quantity of interest (QoI) is approximated in a basis…
A statistical learning strategy for closed-loop control of fluid flows
Florimond Guéniat, Lionel Mathelin, M. Yousuff Hussaini
This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied t…
Using n-grams models for visual semantic place recognition
Mathieu Dubois, Frenoux Emmanuelle, Philippe Tarroux
The aim of this paper is to present a new method for visual place recognition. Our system combines global image characterization and visual words, which allows to use efficient Bay…