Hola,
Es un gusto para mí invitarlos a la defensa de maestría en Ingeniería
Eléctrica de Facundo Lezama, a quien tuve el placer de dirigir junto con
Germán Capdehourat. Se trata de una continuación de la línea de
localización indoor que llevamos hace unos años junto con Germán, esta
vez evaluando métodos de aprendizaje en grafos.
Les paso los detalles abajo.
saludos
Federico
Título: On the Application of Graph Neural Networks for Indoor
Positioning Systems
Tribunal: Pablo Cancela (IIE-FING), Alberto Castro (IIE-INCO-FING),
Fernando Gama (Morgan Stanley).
Fecha y hora: miércoles 23 de noviembre a las 10AM
Lugar: laboratorio de software del IIE. Quienes quieran acceder
virtualmente, se ponen en contacto conmigo.
Resumen: Due to the inability of GPS (Global Positioning System) or
other GNSS (Global Navigation Satellite System) methods to provide
satisfactory precision for the indoor localization scenario, indoor
positioning systems resort to other signals already available on-site,
typically Wi-Fi given its ubiquity. However, instead of relying on an
error-prone propagation model as in ranging methods, the popular
fingerprinting positioning technique considers a more direct
data-driven approach to the problem. First of all, the area of interest
is divided into zones, and then a machine learning algorithm is trained
to map between, for instance, power measurements from Access Points
(APs) to the localization zone, thus effectively turning the problem
into a classification one.
However, although the positioning problem is a geometrical one,
virtually all methods proposed in the literature disregard the
underlying structure of the data, using generic machine learning
algorithms. In this work we consider instead a graph-based learning
method, Graph Neural Networks, a paradigm that has emerged in the last
few years and constitutes the state-of-the-art for several problems.
After presenting the pertinent theoretical background, we discuss two
possibilities to construct the underlying graph for the positioning
problem. We then perform a thorough evaluation of both possibilities,
and compare it with some of the most popular machine learning
alternatives. The main conclusion is that these graph-based methods
obtain systematically better results, particularly with regards to
practical aspects (e.g. gracefully tolerating faulty APs), which makes
them a serious candidate to consider when deploying positioning systems