Fully Bayesian Inference for Finite and Infinite Discrete Exponential Mixture Models

Count data often appears in natural language processing and computer vision applications. For example, in images and textual documents clustering, each image or text can be described by a histogram of visual words or text words. In real applications, these frequency vectors often show high-dimensional and sparsity nature. In this case, hierarchical Bayesian modeling frameworks show the ability to model the dependence of the word repetitive occurrences ’burstiness’. Moreover, approximating these models to exponential families is helpful to improve computing efficiency, especially when facing high-dimensional count data and large data sets. However, classical deterministic approaches such as expectation-maximization (EM) do not achieve good results in real-life complex applications. This thesis explores the use of a fully Bayesian inference for finite discrete exponential mixture models of Multinomial Generalized Dirichlet (EMGD), Multinomial Beta-Liouville (EMBL), Multinomial Scaled Dirichlet (EMSD), and Multinomial Shifted Scaled Dirichlet (EMSSD). Finite mixtures have already shown superior performance in real data sets clustering with EM approach. The proposed approaches in this thesis are based on Monte Carlo simulation technique of Gibbs sampling mixed with Metropolis-Hastings step, and we utilize exponential family conjugate prior information to construct the required posteriors relying on Bayesian theory. Furthermore, we also present the infinite models based on Dirichlet processes, which results in clustering algorithms that do not require the specification of the number of mixture components to be given in advance. The performance of our Bayesian approaches was tested in some challenging real-world applications concerning text sentiment analysis, fake news detection, and human face gender recognition

A Systematic Review on the Detection of Fake News Articles

Currently submitted to ACM Transactions on Intelligent Systems and Technology. Awaiting peer-review.It has been argued that fake news and the spread of false information pose a threat to societies throughout the world, from influencing the results of elections to hindering the efforts to manage the COVID-19 pandemic. To combat this threat, a number of Natural Language Processing (NLP) approaches have been developed. These leverage a number of datasets, feature extraction/selection techniques and machine learning (ML) algorithms to detect fake news before it spreads. While these methods are well-documented, there is less evidence regarding their efficacy in this domain. By systematically reviewing the literature, this paper aims to delineate the approaches for fake news detection that are most performant, identify limitations with existing approaches, and suggest ways these can be mitigated. The analysis of the results indicates that Ensemble Methods using a combination of news content and socially-based features are currently the most effective. Finally, it is proposed that future research should focus on developing approaches that address generalisability issues (which, in part, arise from limitations with current datasets), explainability and bias

Approximate Bayesian Inference for Count Data Modeling

Bayesian inference allows to make conclusions based on some antecedents that depend on prior knowledge. It additionally allows to quantify uncertainty, which is important in Machine Learning in order to make better predictions and model interpretability. However, in real applications, we often deal with complicated models for which is unfeasible to perform full Bayesian inference. This thesis explores the use of approximate Bayesian inference for count data modeling using Expectation Propagation and Stochastic Expectation Propagation. In Chapter 2, we develop an expectation propagation approach to learn an EDCM finite mixture model. The EDCM distribution is an exponential approximation to the widely used Dirichlet Compound distribution and has shown to offer excellent modeling capabilities in the case of sparse count data. Chapter 3 develops an efficient generative mixture model of EMSD distributions. We use Stochastic Expectation Propagation, which reduces memory consumption, important characteristic when making inference in large datasets. Finally, Chapter 4 develops a probabilistic topic model using the generalized Dirichlet distribution (LGDA) in order to capture topic correlation while maintaining conjugacy. We make use of Expectation Propagation to approximate the posterior, resulting in a model that achieves more accurate inference compared to variational inference. We show that latent topics can be used as a proxy for improving supervised tasks