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Gaussian Mixture Models Based on Principal Components and Applications
Joint Authors
Alqahtani, Nada A.
Kalantan, Zakiah I.
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-31
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Data scientists use various machine learning algorithms to discover patterns in large data that can lead to actionable insights.
In general, high-dimensional data are reduced by obtaining a set of principal components so as to highlight similarities and differences.
In this work, we deal with the reduced data using a bivariate mixture model and learning with a bivariate Gaussian mixture model.
We discuss a heuristic for detecting important components by choosing the initial values of location parameters using two different techniques: cluster means, k-means and hierarchical clustering, and default values in the “mixtools” R package.
The parameters of the model are obtained via an expectation maximization algorithm.
The criteria from Bayesian point are evaluated for both techniques, demonstrating that both techniques are efficient with respect to computation capacity.
The effectiveness of the discussed techniques is demonstrated through a simulation study and using real data sets from different fields.
American Psychological Association (APA)
Alqahtani, Nada A.& Kalantan, Zakiah I.. 2020. Gaussian Mixture Models Based on Principal Components and Applications. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1193041
Modern Language Association (MLA)
Alqahtani, Nada A.& Kalantan, Zakiah I.. Gaussian Mixture Models Based on Principal Components and Applications. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1193041
American Medical Association (AMA)
Alqahtani, Nada A.& Kalantan, Zakiah I.. Gaussian Mixture Models Based on Principal Components and Applications. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1193041
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193041