Login | Register

Generative Models Based on the Bounded Asymmetric Student’s t-Distribution


Generative Models Based on the Bounded Asymmetric Student’s t-Distribution

Bouarada, Ons (2023) Generative Models Based on the Bounded Asymmetric Student’s t-Distribution. Masters thesis, Concordia University.

[thumbnail of Bouarada_MASc_S2023.pdf]
Text (application/pdf)
Bouarada_MASc_S2023.pdf - Accepted Version
Available under License Spectrum Terms of Access.


Gaussian mixture models (GMMs) are a very useful and widely popular approach for clustering,
but they have several limitations, such as low outliers tolerance and assumption of data normality. Another problem in relation to finite mixture models in general is the inference of an optimal number of mixture components. An excellent approach to solve this problem is model selection, which is the process of choosing the optimal number of mixture components that ensures the best clustering performance. In this thesis, we attempt to tackle both aforementioned issues: we propose using minimum message length (MML) as a model selection criterion for multivariate bounded
asymmetric Student’s t-mixture model (BASMM). In fact, BASMM is chosen as an alternative to improve the GMM’s limitations, as it provides a better fit for the real-world data irregularities. We
formulate the definition of MML and the BASMM, and we test their performance through multiple experiments with different problem settings.
Hidden Markov models (HMMs) are popular methods for continuous sequential data modeling and classification tasks. In such applications, the observation emission densities of the HMM hidden states are typically modeled by elliptically contoured distributions, namely Gaussians or Student’s t-distributions. In this context, this thesis proposes BAMMHMM: a novel HMM with Bounded Asymmetric Student’s t-Mixture Model (BASMM) emissions. This HMM is destined to sufficiently fit skewed and outlier-heavy observations, which are typical in many fields, such as financial or signal processing-related datasets. We demonstrate the improved robustness of our model
by presenting the results of different real-world applications.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Bouarada, Ons
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Information and Systems Engineering
Date:12 March 2023
Thesis Supervisor(s):Bouguila, Nizar
ID Code:991936
Deposited By: Ons Bouarada
Deposited On:21 Jun 2023 14:32
Last Modified:21 Jun 2023 14:32
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top