Heterogeneous Relational Kernel Learning
This work addresses the limitation of prior relational kernel learning approaches in handling heterogeneous time series, offering incremental improvements for data analysis tasks.
The paper tackled the problem of analyzing heterogeneous time series by extending Bayesian methods to create an interpretable kernel embedding, which was applied to clustering, pattern discovery, and anomaly detection with no added computational cost.
Recent work has developed Bayesian methods for the automatic statistical analysis and description of single time series as well as of homogeneous sets of time series data. We extend prior work to create an interpretable kernel embedding for heterogeneous time series. Our method adds practically no computational cost compared to prior results by leveraging previously discarded intermediate results. We show the practical utility of our method by leveraging the learned embeddings for clustering, pattern discovery, and anomaly detection. These applications are beyond the ability of prior relational kernel learning approaches.