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Learning to sense from events via semantic variational autoencoder.
- Source :
-
PloS one [PLoS One] 2021 Dec 23; Vol. 16 (12), pp. e0260701. Date of Electronic Publication: 2021 Dec 23 (Print Publication: 2021). - Publication Year :
- 2021
-
Abstract
- In this paper, we introduce the concept of learning to sense, which aims to emulate a complex characteristic of human reasoning: the ability to monitor and understand a set of interdependent events for decision-making processes. Event datasets are composed of textual data and spatio-temporal features that determine where and when a given phenomenon occurred. In learning to sense, related events are mapped closely to each other in a semantic vector space, thereby identifying that they contain similar contextual meaning. However, learning a semantic vector space that satisfies both textual similarities and spatio-temporal constraints is a crucial challenge for event analysis and sensing. This paper investigates a Semantic Variational Autoencoder (SVAE) to fine-tune pre-trained embeddings according to both textual and spatio-temporal events of the class of interest. Experiments involving more than one hundred sensors show that our SVAE outperforms a competitive one-class classification baseline. Moreover, our proposal provides desirable learning requirements to sense scenarios, such as visualization of the sensor decision function and heat maps with the sensor's geographic impact.<br />Competing Interests: The authors have declared that no competing interests exist.
Details
- Language :
- English
- ISSN :
- 1932-6203
- Volume :
- 16
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- PloS one
- Publication Type :
- Academic Journal
- Accession number :
- 34941880
- Full Text :
- https://doi.org/10.1371/journal.pone.0260701