Name
Abstract | ||
|---|---|---|
We develop a new class of distances for trajectories, based on the distance to a set of landmarks. These distances easily and interpretably map objects to a Euclidean space, are simple to compute, and perform well in data analysis tasks. For trajectories, they match and in some cases significantly out-perform all state-of-the-art other metrics, can effortlessly be used in k-means clustering, and directly plugged into approximate nearest neighbor approaches which immediately out-perform the best recent advances in trajectory similarity search by several orders of magnitude. These distances do not require complicated alignment (common in trajectory case). We show reasonable and often simple conditions under which these distances are metrics.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3347146.3359098 | SIGSPATIAL/GIS |
Keywords | Field | DocType |
trajectory similarity, trajectory classification, sketching | Computer vision,Computer science,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6909-1 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeff M. Phillips | 1 | 536 | 49.83 |
| Pingfan Tang | 2 | 0 | 1.69 |