High-dimensional Sparse Count Data Clustering Using Finite Mixture Models

Due to the massive amount of available digital data, automating its analysis and modeling for different purposes and applications has become an urgent need. One of the most challenging tasks in machine learning is clustering, which is defined as the process of assigning observations sharing similar characteristics to subgroups. Such a task is significant, especially in implementing complex algorithms to deal with high-dimensional data. Thus, the advancement of computational power in statistical-based approaches is increasingly becoming an interesting and attractive research domain. Among the successful methods, mixture models have been widely acknowledged and successfully applied in numerous fields as they have been providing a convenient yet flexible formal setting for unsupervised and semi-supervised learning. An essential problem with these approaches is to develop a probabilistic model that represents the data well by taking into account its nature. Count data are widely used in machine learning and computer vision applications where an object, e.g., a text document or an image, can be represented by a vector corresponding to the appearance frequencies of words or visual words, respectively. Thus, they usually suffer from the well-known curse of dimensionality as objects are represented with high-dimensional and sparse vectors, i.e., a few thousand dimensions with a sparsity of 95 to 99%, which decline the performance of clustering algorithms dramatically. Moreover, count data systematically exhibit the burstiness and overdispersion phenomena, which both cannot be handled with a generic multinomial distribution, typically used to model count data, due to its dependency assumption. This thesis is constructed around six related manuscripts, in which we propose several approaches for high-dimensional sparse count data clustering via various mixture models based on hierarchical Bayesian modeling frameworks that have the ability to model the dependency of repetitive word occurrences. In such frameworks, a suitable distribution is used to introduce the prior information into the construction of the statistical model, based on a conjugate distribution to the multinomial, e.g. the Dirichlet, generalized Dirichlet, and the Beta-Liouville, which has numerous computational advantages. Thus, we proposed a novel model that we call the Multinomial Scaled Dirichlet (MSD) based on using the scaled Dirichlet as a prior to the multinomial to allow more modeling flexibility. Although these frameworks can model burstiness and overdispersion well, they share similar disadvantages making their estimation procedure is very inefficient when the collection size is large. To handle high-dimensionality, we considered two approaches. First, we derived close approximations to the distributions in a hierarchical structure to bring them to the exponential-family form aiming to combine the flexibility and efficiency of these models with the desirable statistical and computational properties of the exponential family of distributions, including sufficiency, which reduce the complexity and computational efforts especially for sparse and high-dimensional data. Second, we proposed a model-based unsupervised feature selection approach for count data to overcome several issues that may be caused by the high dimensionality of the feature space, such as over-fitting, low efficiency, and poor performance. Furthermore, we handled two significant aspects of mixture based clustering methods, namely, parameters estimation and performing model selection. We considered the Expectation-Maximization (EM) algorithm, which is a broadly applicable iterative algorithm for estimating the mixture model parameters, with incorporating several techniques to avoid its initialization dependency and poor local maxima. For model selection, we investigated different approaches to find the optimal number of components based on the Minimum Message Length (MML) philosophy. The effectiveness of our approaches is evaluated using challenging real-life applications, such as sentiment analysis, hate speech detection on Twitter, topic novelty detection, human interaction recognition in films and TV shows, facial expression recognition, face identification, and age estimation

Categorisation of 3D objects in range images using compositional hierarchies of parts based on MDL and entropy selection criteria

Abstract. This paper presents a new approach to object categorisation in range images using our novel hierarchical compositional representa-tion of surfaces. The atomic elements at the bottom layer of the hierar-chy encode quantized relative depth of pixels in a local neighbourhood. Subsequent layers are formed in the recursive manner, each higher layer is statistically learnt on the layer below via a growing receptive field. In this paper we mainly focus on the part selection problem, i.e. the choice of the optimisation criteria which provide the information on which parts should be promoted to the higher layer of the hierarchy. Namely, two methods based on Minimum Description Length and category based en-tropy are introduced. The proposed approach was extensively tested on two widely-used datasets for object categorisation with results that are of the same quality as the best results achieved for those datasets

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen