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Local Optima in Mixture Modeling.

Title: Local Optima in Mixture Modeling.
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Name(s): Shireman, Emilie M, author
Steinley, Douglas, author
Brusco, Michael J, author
Type of Resource: text
Genre: Journal Article
Text
Date Issued: 2016-07-01
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are presented that examine the effect of a variety of data structures on the propensity of local optima and the classification quality of the resulting solution. We show that there is a moderately strong relationship between a solution that has a high proportion of local optima and one that is poorly classified.
Identifier: FSU_pmch_27494191 (IID), 10.1080/00273171.2016.1160359 (DOI), PMC5534344 (PMCID), 27494191 (RID), 27494191 (EID)
Keywords: EM algorithm, Mixture modeling, Local optima
Grant Number: R01 AA023248, T32 AA013526
Publication Note: This NIH-funded author manuscript originally appeared in PubMed Central at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534344.
Subject(s): Algorithms
Computer Simulation
Models, Statistical
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_pmch_27494191
Host Institution: FSU
Is Part Of: Multivariate behavioral research.
1532-7906
Issue: iss. 4, vol. 51

Choose the citation style.
Shireman, E. M., Steinley, D., & Brusco, M. J. (2016). Local Optima in Mixture Modeling. Multivariate Behavioral Research. Retrieved from http://purl.flvc.org/fsu/fd/FSU_pmch_27494191