Name
Abstract | ||
|---|---|---|
The Adaptive Mean Shift (AMS) algorithm is a popular and simple non-parametric clustering approach based on Kernel Density Estimation. In this paper the AMS is reformulated in a Bayesian framework, which permits a natural generalization in several directions and is shown to improve performance. The Bayesian framework considers the AMS to be a method of obtaining a posterior mode. This allows the algorithm to be generalized with three components which are not considered in the conventional approach: node weights, a prior for a particular location, and a posterior distribution for the bandwidth. Practical methods of building the three different components are considered. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/IFS-162103 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Adaptive mean shift algorithm,kernel density estimation | Pattern recognition,Artificial intelligence,Kernel adaptive filter,Mean-shift,Variable kernel density estimation,Machine learning,Mathematics,Kernel density estimation,Bayesian probability | Journal |
Volume | Issue | ISSN |
30 | 6 | 1064-1246 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ji Won Yoon | 1 | 112 | 23.94 |