Bayesian interpretation to generalize adaptive mean shift algorithm
- Authors
- Yoon, Ji Won
- Issue Date
- 2016
- Publisher
- IOS PRESS
- Keywords
- Adaptive mean shift algorithm; kernel density estimation
- Citation
- JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, v.30, no.6, pp.3583 - 3592
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
- Volume
- 30
- Number
- 6
- Start Page
- 3583
- End Page
- 3592
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/90192
- DOI
- 10.3233/IFS-162103
- ISSN
- 1064-1246
- 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.
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