Early and reliable event detection using proximity space representation
Résumé
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.
Mots clés
Training algorithms
Theoretical guarantees
State-of-the-art algorithms
Similarity functions
Partial observation
Incomplete information
Learning algorithms
Learning systems
Decision functions
Generalization ability
Economic and social effects
Artificial intelligence
machine learning
artificial intelligence
signal processing
Origine : Fichiers produits par l'(les) auteur(s)