Bayesian interpretation to generalize adaptive mean shift algorithm

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish
    Pages (from-to)3583-3592
    Number of pages10
    JournalJournal of Intelligent and Fuzzy Systems
    Volume30
    Issue number6
    DOIs
    Publication statusPublished - 2016 Apr 30

    Bibliographical note

    Publisher Copyright:
    © 2016 - IOS Press and the authors. All rights reserved.

    Keywords

    • Adaptive mean shift algorithm
    • kernel density estimation

    ASJC Scopus subject areas

    • Statistics and Probability
    • General Engineering
    • Artificial Intelligence

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