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Early and reliable event detection using proximity space representation

Maxime Sangnier 1 J. Gauthier 2 A. Rakotomamonjy 3
2 LADIS - Laboratoire d'analyse des données et d'intelligence des systèmes
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
3 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : Let us consider a specific action or situation (called event) that takes place within a time series. The objective in early detection is to build a decision function that is able to go off as soon as possible from the onset of an occurrence of this event. This implies making a decision with an incomplete information. This paper proposes a novel framework that i) guarantees that a de-tection made with a partial observation will also occur at full observation of the time-series; ii) incorporates in a consistent manner the lack of knowledge about the minimal amount of information needed to make a decision. The proposed detector is based on mapping the temporal sequences to a landmarking space thanks to appropriately designed similarity functions. As a by-product, the framework benefits from a scalable training algorithm and a theoretical guarantee concerning its generalization ability. We also discuss an important improvement of our framework in which decision function can still be made reliable while being more expressive. Our experimental studies provide compelling results on toy data, presenting the trade-off that occurs when aiming at accuracy, earliness and reliability. Results on real physiological and video datasets show that our proposed approach is as accurate and early as state-of-the-art algorithm, while ensuring reliability and being far more efficient to learn.
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Submitted on : Wednesday, July 18, 2018 - 3:55:53 PM
Last modification on : Tuesday, October 19, 2021 - 5:07:29 PM


  • HAL Id : cea-01843181, version 1


Maxime Sangnier, J. Gauthier, A. Rakotomamonjy. Early and reliable event detection using proximity space representation. 33rd International Conference on Machine Learning, Jun 2016, New York, United States. pp.2310-2319. ⟨cea-01843181⟩



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