Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DSW.2019.8755565 | 2019 IEEE Data Science Workshop (DSW) |
Keywords | Field | DocType |
directed information,adaptive directed information,temporal modeling,data exploration,interaction mining | Data exploration,Multivariate statistics,Computer science,Robust statistics,Temporal modeling,Drone,Artificial intelligence,Exploratory data analysis,Ensemble learning,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-0709-7 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brandon Oselio | 1 | 4 | 4.14 |
| Alfred O. Hero III | 2 | 2600 | 301.12 |
| Amir Abbas Sadeghian | 3 | 135 | 7.05 |
| Silvio Savarese | 4 | 3975 | 161.69 |