Comparison of methods for initializing EM algorithm for estimation of parameters of Gaussian, multi-component, heteroscedastic mixture models

No abstract is available for this paper.

• Publication year

  2013

• Venue

  Unknown venue

• Publication date

  2013-02-09

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.21936/SI2013_V34.N1.6
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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  2009cited by this paper
• Initializing Partition-Optimization Algorithms

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  2007cited by this paper
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  2006cited by this paper
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  2006cited by this paper
• Model-based cluster and discriminant analysis with the MIXMOD software

  2006cited by this paper
• Genetic-based EM algorithm for learning Gaussian mixture models

  2005cited by this paper
• Asymptotic convergence properties of the EM algorithm with respect to the overlap in the mixture

  2005cited by this paper
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction

  2004cited by this paper
• Initializing EM using the properties of its trajectories in Gaussian mixtures

  2004cited by this paper
• A likelihood-based constrained algorithm for multivariate normal mixture models

  2004cited by this paper
• Editorial: recent developments in mixture models

  2003cited by this paper
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models

  2003influential reference
• Choosing initial values for the EM algorithm for finite mixtures

  2003influential reference
• Applying the EM-algorithm to Classification of Bacteria

  2000cited by this paper
• The EMMIX software for the fitting of mixtures of normal and t-components

  1999cited by this paper
• Initialization of Iterative Refinement Clustering Algorithms

  1998cited by this paper
• An Experimental Comparison of Several Clustering and Initialization Methods

  1998cited by this paper
• On Convergence Properties of the EM Algorithm for Gaussian Mixtures

  1996cited by this paper
• The EM algorithm and extensions

  1996influential reference
• Summary of the

  1994cited by this paper
• Maximum Likelihood and the EM Algorithm for 2D NMR Spectroscopy

  1993cited by this paper
• the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.

  1991cited by this paper
• Handbook Of Genetic Algorithms

  1990cited by this paper
• A constrained EM algorithm for univariate normal mixtures

  1986cited by this paper
• A Constrained Formulation of Maximum-Likelihood Estimation for Normal Mixture Distributions

  1985cited by this paper
• ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM

  1983cited by this paper
• Finite Mixture Distributions

  1981cited by this paper
• An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions

  1978cited by this paper
• Percentage Points of a Test for Clusters

  1969cited by this paper
• A Dynamic Programming Algorithm for Cluster Analysis

  1969cited by this paper
• On Grouping for Maximum Homogeneity

  1958cited by this paper
• CONSISTENCY OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE PRESENCE OF INFINITELY MANY INCIDENTAL PARAMETERS

  1956cited by this paper
• Computational Statistics and Data Analysis Learning Mixture Models via Component-wise Parameter Smoothing

  year unknowncited by this paper

• A Survey of Selected Machine Learning Methods for the Segmentation of Raw Motion Capture Data into Functional Body Mesh

  2016cites this paper
• Initializing EM algorithm for univariate Gaussian, multi-component, heteroscedastic mixture models by dynamic programming partitions

  2015cites this paper
• Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

  2015cites this paper
• Least Squares Estimators of Peptide Species Concentrations Based on Gaussian Mixture Decompositions of Protein Mass Spectra

  2015cites this paper