5 research outputs found
Maximum likelihood estimation of robust constrained Gaussian mixture models
Ankara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Ph. D.) -- Bilkent University, 2013.Includes bibliographical references leaves 155-170.Density estimation using Gaussian mixture models presents a fundamental
trade off between the flexibility of the model and its sensitivity to the unwanted/unmodeled
data points in the data set. The expectation maximization
(EM) algorithm used to estimate the parameters of Gaussian mixture models is
prone to local optima due to nonconvexity of the problem and the improper selection
of parameterization. We propose a novel modeling framework, three different
parameterizations and novel algorithms for the constrained Gaussian mixture
density estimation problem based on the expectation maximization algorithm,
convex duality theory and the stochastic search algorithms. We propose a new
modeling framework called Constrained Gaussian Mixture Models (CGMM) that
incorporates prior information into the density estimation problem in the form
of convex constraints on the model parameters. In this context, we consider two
different parameterizations where the first set of parameters are referred to as the
information parameters and the second set of parameters are referred to as the
source parameters. To estimate the parameters, we use the EM algorithm where
we solve two optimization problems alternatingly in the E-step and the M-step.
We show that the M-step corresponds to a convex optimization problem in the
information parameters. We form a dual problem for the M-step and show that
the dual problem corresponds to a convex optimization problem in the source
parameters. We apply the CGMM framework to two different problems: Robust
density estimation and compound object detection problems. In the robust density
estimation problem, we incorporate the inlier/outlier information available
for small number of data points as convex constraints on the parameters using
the information parameters. In the compound object detection problem, we incorporate
the relative size, spectral distribution structure and relative location
relations of primitive objects as convex constraints on the parameters using the
source parameters. Even with the propoper selection of the parameterization, density estimation problem for Gaussian mixture models is not jointly convex
in both the E-step variables and the M-step variables. We propose a third parameterization
based on eigenvalue decomposition of covariance matrices which is
suitable for stochastic search algorithms in general and particle swarm optimization
(PSO) algorithm in particular. We develop a new algorithm where global
search skills of the PSO algorithm is incorporated into the EM algorithm to do
global parameter estimation. In addition to the mathematical derivations, experimental
results on synthetic and real-life data sets verifying the performance of
the proposed algorithms are provided.Arı, ÇağlarPh.D
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
Abstracts on Radio Direction Finding (1899 - 1995)
The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography).
Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM.
The contents of these files are:
1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format];
2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format];
3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion