1 research outputs found
Semi-Supervised Online Structure Learning for Composite Event Recognition
Online structure learning approaches, such as those stemming from Statistical
Relational Learning, enable the discovery of complex relations in noisy data
streams. However, these methods assume the existence of fully-labelled training
data, which is unrealistic for most real-world applications. We present a novel
approach for completing the supervision of a semi-supervised structure learning
task. We incorporate graph-cut minimisation, a technique that derives labels
for unlabelled data, based on their distance to their labelled counterparts. In
order to adapt graph-cut minimisation to first order logic, we employ a
suitable structural distance for measuring the distance between sets of logical
atoms. The labelling process is achieved online (single-pass) by means of a
caching mechanism and the Hoeffding bound, a statistical tool to approximate
globally-optimal decisions from locally-optimal ones. We evaluate our approach
on the task of composite event recognition by using a benchmark dataset for
human activity recognition, as well as a real dataset for maritime monitoring.
The evaluation suggests that our approach can effectively complete the missing
labels and eventually, improve the accuracy of the underlying structure
learning system