• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
      • View Item
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Iterative estimation of Robust Gaussian mixture models in heterogeneous data sets

      Thumbnail
      View / Download
      1.2 Mb
      Author
      Mercan, Caner
      Advisor
      Aksoy, Selim
      Date
      2014-07
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
      101
      views
      448
      downloads
      Abstract
      Density estimation is the process of estimating the parameters of a probability density function from data. The Gaussian mixture model (GMM) is one of the most preferred density families. We study the estimation of a Gaussian mixture from a heterogeneous data set that is de ned as the set of points that contains interesting points that are sampled from a mixture of Gaussians as well as non-Gaussian distributed uninteresting ones. The traditional GMM estimation techniques such as the Expectation-Maximization algorithm cannot e ectively model the interesting points in a heterogeneous data set due to their sensitivity to the uninteresting points as outliers. Another potential problem is that the true number of components should often be known a priori for a good estimation. We propose a GMM estimation algorithm that iteratively estimates the number of interesting points, the number of Gaussians in the mixture, and the actual mixture parameters while being robust to the presence of uninteresting points in heterogeneous data. The procedure is designed so that one Gaussian component is estimated using a robust formulation at each iteration. The number of interesting points that belong to this component is also estimated using a multi-resolution search procedure among a set of candidates. If a hypothesis on the Gaussianity of these points is accepted, the estimated Gaussian is kept as a component in the mixture, the associated points are removed from the data set, and the iterations continue with the remaining points. Otherwise, the estimation process is terminated and the remaining points are labeled as uninteresting. Thus, the stopping criterion helps to identify the true number of components without any additional information. Comparative experiments on synthetic and real-world data sets show that our algorithm can identify the true number of components and can produce a better density estimate in terms of log-likelihood compared to two other algorithms.
      Keywords
      Gaussian Mixture model
      Robust Gaussian estimation
      Identifying number of mixture components
      Iterative Gaussian mixture estimation
      Permalink
      http://hdl.handle.net/11693/28945
      Collections
      • Dept. of Computer Engineering - Master's degree 516
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy