Time-Varying Interaction Estimation Using Ensemble Methods
This work addresses the challenge of non-stationary interaction estimation for researchers in fields like computer vision and data analysis, but it appears incremental as it builds on existing adaptive directed information methods.
The paper tackled the problem of estimating time-varying interactions in multivariate data by introducing an ensemble method to improve the robustness of adaptive directed information, and demonstrated its application on the Stanford drone dataset for crowded scene analysis.
Directed information (DI) is a useful tool to explore time-directed interactions in multivariate data. However, as originally formulated DI is not well suited to interactions that change over time. In previous work, adaptive directed information was introduced to accommodate non-stationarity, while still preserving the utility of DI to discover complex dependencies between entities. There are many design decisions and parameters that are crucial to the effectiveness of ADI. Here, we apply ideas from ensemble learning in order to alleviate this issue, allowing for a more robust estimator for exploratory data analysis. We apply these techniques to interaction estimation in a crowded scene, utilizing the Stanford drone dataset as an example.