Early and reliable event detection using proximity space representation - Archive ouverte HAL Access content directly
Conference Papers Year : 2016

Early and reliable event detection using proximity space representation

(1) , (2) , (3)
1
2
3

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.
Fichier principal
Vignette du fichier
article_MaximeSangnier_icml2016.pdf (366.27 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

cea-01843181 , version 1 (09-05-2022)

Identifiers

  • HAL Id : cea-01843181 , version 1

Cite

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

Share

Gmail Facebook Twitter LinkedIn More