Name
Abstract | ||
|---|---|---|
In this paper, the limitation that is prominent in most existing works of change-point detection methods is addressed by proposing a nonparametric, computationally efficient method. The limitation is that most works assume that each data point observed at each time step is a single multi-dimensional vector. However, there are many situations where this does not hold. Therefore, a setting where each observation is a collection of random variables, which we call a bag of data, is considered. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TKDE.2015.2426693 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
gaussian distribution,earth mover s distance,earth,parametric statistics,random variables,data models,change point detection,anomaly detection,earth movers distance | Data modeling,Anomaly detection,Data mining,Earth mover's distance,Change detection,Computer science,Nonparametric statistics,Parametric statistics,Artificial intelligence,Metric space,Machine learning,Estimator | Conference |
Volume | Issue | ISSN |
PP | 99 | 1041-4347 |
Citations | PageRank | References |
1 | 0.35 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kensuke Koshijima | 1 | 4 | 0.82 |
| Hideitsu Hino | 2 | 99 | 25.73 |
| Noboru Murata | 3 | 855 | 170.36 |