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Generative Models Based on the Bounded Asymmetric Gaussian Distribution

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Generative Models Based on the Bounded Asymmetric Gaussian Distribution

Xian, Zixiang (2021) Generative Models Based on the Bounded Asymmetric Gaussian Distribution. Masters thesis, Concordia University.

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Abstract

The bounded asymmetric Gaussian mixture model (BAGMM) has proved that it generally performs better than the classical Gaussian mixture model. In this thesis, we investigate the learning of the BAGMM. Indeed, we propose an Expectation-Maximization (EM) algorithm to estimate the model parameters. A model selection criterion for BAGMM using minimum message length (MML) is proposed to determine the optimal number of clusters. The MML is shown to perform better than other model selection criteria.
In this thesis, we additionally propose an unsupervised feature selection framework using BAGMM to determine the structure of high dimensional data without knowing in advance the number of clusters nor the importance of the involved features. The validation for this framework involves several human-related recognition challenges, such as human activity categorization and human gender recognition.
Finally, we integrate the BAGMM into a hidden Markov model (HMM) framework, which uses BAGMM to model the emission probability distribution. The BAGMM-based HMM is evaluated with several real-world applications and compared with other Gaussian mixture-based HMMs.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Xian, Zixiang
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Information Systems Security
Date:20 August 2021
Thesis Supervisor(s):Nizar, Bouguila and Manar, Amayri
ID Code:988728
Deposited By: Zixiang Xian
Deposited On:29 Nov 2021 16:29
Last Modified:29 Nov 2021 16:29
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