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Accurate and timely occupancy prediction has the potential to improve the efficiency of energy management systems in smart buildings. Occupancy prediction heavily depends on historical occupancy-related data collected from various sensor sources. Unfortunately, a major problem in that context is the difficulty to collect training data. This situation inspired us to rethink the occupancy prediction problem, proposing the use of an original principled approach based on occupancy estimation via interactive learning to collect the needed training data. Following that, the collected data, along with various features, were fed into several algorithms to predict future occupancy. This paper mainly proposes a weakly supervised occupancy prediction framework based on office sensor readings and occupancy estimations derived from an interactive learning approach. Two studies are the main emphasis of this paper. The first is the prediction of three occupancy states, referred to as discrete states: absence, presence of one occupant, and presence of more than one occupant. The purpose of the second study is to anticipate the future number of occupants, i.e., continuous states. Extensive simulations were run to demonstrate the merits of the proposed prediction framework's performance and to validate the interactive learning-based approach's ability to contribute to the achievement of effective occupancy prediction. The results reveal that LightGBM, a machine learning model, is a better fit for short-term predictions than known recursive neural networks when dealing with a limited dataset. For a 24 h window forecast, LightGBM improved accuracy from 38% to 50%, which is an excellent result for non-aggregated data (single office).

Background: Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths., Methods: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤ 7.20-acidosis, n = 18; pH > 7.20 and pH < 7.25-foetal deterioration, n = 4; or clinical decision without evidence of pathological outcome measures, n = 24). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures., Results: The findings show that deep learning classification achieves sensitivity = 94%, specificity = 91%, Area under the curve = 99%, F-score = 100%, and mean square error = 1%., Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies.

We introduce a mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. Based on this framework, we propose model-based classification and model-based clustering algorithms. We develop an objective function for the minimum message length (MML) model selection criterion to discover the optimal number of clusters for the unsupervised approach of our proposed model. Given the crucial attention received by Explainable AI (XAI) in recent years, we introduce a method to interpret the predictions obtained from the proposed model in both learning settings by defining their boundaries in terms of the crucial features. Integrating Explainability within our proposed algorithm increases the credibility of the algorithm's predictions since it would be explainable to the user's perspective through simple If-Then statements using a small binary decision tree. In this paper, the proposed algorithm proves its reliability and superiority to several state-of-the-art machine learning algorithms within the following real-world applications: fault detection and diagnosis (FDD) in chillers, occupancy estimation and categorization of residential energy consumers. [ABSTRACT FROM AUTHOR]

Short text clustering is one of the fundamental tasks in natural language processing. Different from traditional documents, short texts are ambiguous and sparse due to their short form and the lack of recurrence in word usage from one text to another, making it very challenging to apply conventional machine learning algorithms directly. In this article, we propose two novel approaches for short texts clustering: collapsed Gibbs sampling infinite generalized Dirichlet multinomial mixture model infinite GSGDMM) and collapsed Gibbs sampling infinite Beta‐Liouville multinomial mixture model (infinite GSBLMM). We adopt two flexible and practical priors to the multinomial distribution where in the first one the generalized Dirichlet distribution is integrated, while the second one is based on the Beta‐Liouville distribution. We evaluate the proposed approaches on two famous benchmark datasets, namely, Google News and Tweet. The experimental results demonstrate the effectiveness of our models compared to basic approaches that use Dirichlet priors. We further propose to improve the performance of our methods with an online clustering procedure. We also evaluate the performance of our methods for the outlier detection task, in which we achieve accurate results. [ABSTRACT FROM AUTHOR]

Count data are commonly exploited in machine learning and computer vision applications; however, they often suffer from the well-known curse of dimensionality, which declines the performance of clustering algorithms dramatically. Feature selection is a major technique for handling a large number of features, which most are often redundant and noisy. In this paper, we propose a probabilistic approach for count data based on the concept of feature saliency in the context of mixture-based clustering using the generalized Dirichlet multinomial distribution. The saliency of irrelevant features is reduced toward zero by minimizing the message length, which equates to doing feature and model selection simultaneously. It is proved that the developed approach is effective in identifying both the optimal number of clusters and the most important features, and so enhancing clustering performance significantly, using a range of challenging applications including text and image clustering. [ABSTRACT FROM AUTHOR]

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled from a mixture of Watson distributions with an infinite number of components that are allowed to be shared across different groups. First, we propose a hierarchical nonparametric Bayesian model for modeling grouped axial data based on the hierarchical Pitman-Yor process mixture model of Watson distributions. Then, we demonstrate that by setting the discount parameters of the proposed model to 0, another hierarchical nonparametric Bayesian model based on hierarchical Dirichlet process can be derived for modeling axial data. To learn the proposed models, we systematically develop a closed-form optimization algorithm based on the collapsed variational Bayes (CVB) inference. Furthermore, to ensure the convergence of the proposed learning algorithm, an annealing mechanism is introduced to the framework of CVB inference, leading to an averaged collapsed variational Bayes inference strategy. The merits of the proposed models for modeling grouped axial data are demonstrated through experiments on both synthetic data and real-world applications involving gene expression data clustering and depth image analysis. [ABSTRACT FROM AUTHOR]

Clustering is an attractive method to handle large-scale data which are explosively generated through digitization. This approach is specifically appropriate when labeling is very costly. In this paper, we constructed an unsupervised learning algorithm and focused on a finite mixture model based on multivariate Beta distribution. Our motivation is the flexibility and high potential that this distribution offers in modeling data. To learn this mixture model, we used an expectation propagation inference framework in which the parameters and the complexity of the model were evaluated concurrently in a single optimization framework. We evaluated the performance of our framework on publicly available datasets related to forgery detection, EEG-based sentiment analysis and human activity recognition. Our proposed model demonstrates comparable results to similar alternatives. [ABSTRACT FROM AUTHOR]

Urban Building Energy Models (UBEMs) are vital for estimating building energy use and related greenhouse gas emissions. However, their reliability needs boosting by using more real-world data, especially regarding occupancy behavior. Presently, UBEMs often use standard occupancy patterns, which may not reflect the real building use, especially for commercial and institutional buildings. Wi-Fi sensing is a reliable approach that can improve UBEMs due to its wide availability and cost-effectiveness. In this study, we leverage detailed signal data derived from Wi-Fi sensing technology to create a realistic, scalable and cost-effective occupancy model. A framework has been developed to derive the number of people in a building, which will influence energy usage patterns and enhance UBEMs. The developed model employs various machine learning techniques and achieves a test accuracy of 77 %. Limited availability and diversity of the initial dataset necessitated the use of data augmentation techniques, enabling the model to learn varied representations and thus achieve better test performance of 91 % post-augmentation. To evaluate the effectiveness of the developed model, it has been applied to two institutional buildings of a specific inner-city district in Montreal, Canada, to compute their heating and cooling demands. The outcomes are then compared with those obtained using standard schedules, revealing a considerable discrepancy in annual peak and total annual cooling demand of about %5 and 20 % respectively. • WiFi sensing data collection from various building use types. • Development of an occupant counting model using data derived from WiFi sensing technology. • Applying of the developed model for estimating the occupancy schedule of an institutional building. • Comparison of the estimated occupancy schedule with standard schedules. • Compare district heating/cooling demand: estimated vs. standard schedule results. [ABSTRACT FROM AUTHOR]

Explainable AI (Artificial Intelligence) has gained significant popularity in recent years. It provides reliable and helpful insights about the model and its output predictions. Even though Explainable AI is an important topic thanks to its interpretability, there have been only very few works that are conducted under the unsupervised learning task. This research aims to apply an interpretable unsupervised machine learning method to the task of Occupancy Estimation (OE). Estimating the number of occupants in buildings has been proven to be helpful to several applications such as energy management. Explainable AI increases the credibility of the applications based on OE by giving the user an interpretation of the obtained OE predictions using simple If-Then rules. In all previous research works on clustering methods either results were so difficult to interpret or data were hard to label. To avert these problems and enhance the understanding of the clustering decision, we propose a small binary decision tree to divide the dataset into k clusters so that the interpretability of the obtained k clusters will be simple to understand. It is known that with an increase in the number of leaves of the decision tree, the accuracy of the clusters increases, the cost of clustering increases, and the interpretability of the obtained clusters will be more difficult to deal with. In this work, we applied and tested an unsupervised machine learning model known as the explainable K-means clustering (ExKMC) algorithm for occupancy estimation. ExKMC by default creates a small tree with k leaves that partitions the data into k clusters, and it also outputs a new tree with k ' leaves where k ' ≥ k that provides explainable clusters. The considered method makes a simple trade-off between the accuracy of prediction and the interpretability of the clustering decisions. [ABSTRACT FROM AUTHOR]

Unsupervised learning has been one of the essentials of pattern recognition and data mining. The role of Dirichlet family of mixture models in this field is inevitable. In this article, we propose a finite Inverted Dirichlet mixture model for unsupervised learning using variational inference. In particular, we develop an incremental algorithm with a component splitting approach for local model selection, which makes the clustering algorithm more efficient. We illustrate our model and learning algorithm with synthetic data and some real applications for occupancy estimation in smart homes and topic learning in images and videos. Extensive comparisons with comparable recent approaches have shown the merits of our proposed model. [ABSTRACT FROM AUTHOR]

Technological advances led to the generation of large scale complex data. Thus, extraction and retrieval of information to automatically discover latent pattern have been largely studied in the various domains of science and technology. Consequently, machine learning experienced tremendous development and various statistical approaches have been suggested. In particular, data clustering has received a lot of attention. Finite mixture models have been revealed to be one of the flexible and popular approaches in data clustering. Considering mixture models, three crucial aspects should be addressed. The first issue is choosing a distribution which is flexible enough to fit the data. In this paper, a model based on multivariate Beta distributions is proposed. The two other challenges in mixture models are estimation of model's parameters and model complexity. To tackle these challenges, variational inference techniques demonstrated considerable robustness. In this paper, two methods are studied, namely, batch and online variational inferences and the models are evaluated on four medical applications including image segmentation of colorectal cancer, multi‐class colon tissue analysis, digital imaging in skin lesion diagnosis and computer aid detection of Malaria. [ABSTRACT FROM AUTHOR]

The wireless industry is moving towards developing smart cellular architectures that dynamically adjust the use of the network elements according to the service demand, and automating their operations in order to minimize both capital expenditure (CAPEX) and operation expenditure (OPEX). This involves developing efficient and unsupervised radio access network (RAN) planning, which has a direct impact on the system performance and CAPEX. This intelligent cellular planning aims at providing the base stations (BSs) configurations (e.g., coverage, user associations and antenna radiation pattern) that minimize the number of deployed BSs and meet the requirements in terms of coverage and capacity. The cellular planning optimization problem has been shown to be complex and non-scalable. Moreover, most of the existing cellular planning techniques result in an over or under provisioning architecture. Motivated by the above, we propose in this paper a novel and efficient unsupervised planning process. We make use of statistical machine learning (SML) to solve the problem at hand. The core idea of SML is that the planning parameters are treated as random variables. The parameters that maximize the corresponding joint probability distribution, conditioned on observations of users’ positions, are learned or inferred using Gibbs sampling theory and Bayes’ theory. To apply this theory to the planning problem, we make significant efforts to properly formulate the problem to be able to incorporate the constraints into the inference process and extract the planning parameters from the inferred model. Through several numerical examples, we compare the performance of the proposed approach to clustering-based and optimization-based existing planning approaches, and demonstrate the efficacy of our approach. We also demonstrate how our approach can leverage existing cellular infrastructures into the new design. [ABSTRACT FROM AUTHOR]

This paper proposes an unsupervised algorithm for learning a finite mixture of scaled Dirichlet distributions. Parameters estimation is based on the maximum likelihood approach, and the minimum message length (MML) criterion is proposed for selecting the optimal number of components. This research work is motivated by the flexibility issues of the Dirichlet distribution, the widely used model for multivariate proportional data, which has prompted a number of scholars to search for generalizations of the Dirichlet. By introducing the extra parameters of the scaled Dirichlet, several useful statistical models could be obtained. Experimental results are presented using both synthetic and real datasets. Moreover, challenging real‐world applications are empirically investigated to evaluate the efficiency of our proposed statistical framework. [ABSTRACT FROM AUTHOR]

We propose in this paper to recognize human activities through an unsupervised learning of finite multivariate generalized Gaussian mixture model. We address an important cue in finite mixture model which is the estimation of the mixture model's parameters for a full covariance matrix. We have developed a novel learning algorithm based on Fixed-point covariance matrix estimator combined with the Expectation-Maximization algorithm. Furthermore, we have proposed an appropriate minimum message length (MML) criterion to deal with model selection problem. We evaluated our proposed method on synthetic datasets and a challenging application namely : Human activity recognition from images and videos. The obtained resutls show clearly the merits of our proposed framework which has better capabilities with full covariance matrix when modeling correlated data. [ABSTRACT FROM AUTHOR]

Developing effective machine learning methods for multimedia data modeling continues to challenge computer vision scientists. The capability of providing effective learning models can have significant impact on various applications. In this work, we propose a nonparametric Bayesian approach to address simultaneously two fundamental problems, namely clustering and feature selection. The approach is based on infinite generalized Dirichlet (GD) mixture models constructed through the framework of Dirichlet process and learned using an accelerated variational algorithm that we have developed. Furthermore, we extend the proposed approach using another nonparametric Bayesian prior, namely Pitman-Yor process, to construct the infinite generalized Dirichlet mixture model. Our experiments, which were conducted through synthetic data sets, the clustering analysis of real-world data sets and a challenging application, namely automatic human action recognition, indicate that the proposed framework provides good modeling and generalization capabilities. [ABSTRACT FROM AUTHOR]

Highlights • Proposition of a new similarity measure for Dirichlet and generalized Dirichlet HMMs (two variants). • Not trivial generalization of existing parametric similarity measure. • Proposition of quality scores for performance characterization of the similarity measures. • Extensive experiments on synthetic data highlighting the performance on different aspects of the newly proposed and state-of-the-art measures. • Illustration of newly proposed similarity measure performance on real-world data sets. Abstract Approaches to design metrics between hidden Markov models (HMM) can be divided into two classes: data-based and parameter-based. The latter has the clear advantage of being deterministic and faster but only a very few similarity measures that can be applied to mixture-based HMMs have been proposed so far. Most of these metrics apply to the discrete or Gaussian HMMs and no comparative study have been led to the best of our knowledge. With the recent development of HMMs based on the Dirichlet and generalized Dirichlet distributions for proportional data modeling, we propose to design three new parametric similarity measures between these HMMs. Extensive experiments on synthetic data show the reliability of these new measures where the existing ones fail at giving expected results when some parameters vary. Illustration on real data show the clustering capability of these measures and their potential applications. [ABSTRACT FROM AUTHOR]

Energy-efficient buildings have gained increasing interest in the last decades as they provide optimal energy management. With the emergence of smart homes, many smart tools have been developed to optimize energy efficiency such as activities recognition (AR) and occupancy estimation (OE). The creation of these smart building tools may be disrupted by the scarcity of labeled data. Indeed, data labeling is a tedious and time-consuming task and, it can be very expensive for building objectives. However, labeled data scarcity can be solved by sharing knowledge from different domains using unsupervised domain adaptation techniques. Also, privacy issues can emerge and prevent the use of certain types of data that share residents' attitudes. However, data privacy can be preserved by considering approaches that do not have direct access to original data. In this research, we provide a comparative analysis between unsupervised domain adaptation (UDA) methods, applied to the tasks of AR and OE, with and without direct access to source domain data. We have 6 adapted approaches for UDA with access to labeled source data: domain separation networks (DSN), cluster alignment with a teacher (CAT), CAT+ gradient reversal (RevGrad), CAT + robust RevGrad (rRevGrad), Auxiliary Target Domain-Oriented Classifier (ATDOC) with nearest centroid classifier (NC), and ATDOC with neighborhood aggregation (NA). Also, we have 6 adapted methods for UDA without labeled source data: confidence score weighting adaptation using joint model data structure (CoWA-JMDS), CoWA-JMDS without weights mixup, divide and contrast (DaC), attracting and dispersing (AaD), source hypothesis transfer with information maximization (SHOT-IM), and source hypothesis transfer with self-supervised pseudo-labeling (SHOT-Pseudo-labeling). All the considered methods have been tested on AR and OE datasets collected using ambient sensors. The comparative analysis made in this work has shown us impressive findings and has given great ideas about the type of approaches (with access or without access to the source data) that we should consider for real-world smart building applications. • A comparative analysis between unsupervised domain adaptation methods is performed. • Using sensor data, five activities and three levels of occupancy have been predicted. • Unsupervised domain adaptation without access to source data solves privacy issues. • Poor performance caused by unbalanced label proportions has been considered. • UDA solves the problem of labeled data scarcity with accuracy up to 89%. [ABSTRACT FROM AUTHOR]

During the last few decades, many statistical approaches that were developed in the fields of computer vision and pattern recognition are based on mixture models. A mixture-based representation has a number of advantages: mixture models are generative, flexible, plus they can take prior information into account to improve the generalization capability. The mixture models that we consider in this paper are based on the Dirichlet and generalized Dirichlet distributions that have been widely used to represent proportional data. The novel aspect of this paper is to develop an entropy-based framework to learn these mixture models. Specifically, we propose a Bayesian framework for model learning by means of a sophisticated entropy-based variational Bayes technique. We present experimental results to show that the proposed method is effective in several applications namely person identity verification, 3D object recognition, text document clustering, and gene expression categorization. [ABSTRACT FROM AUTHOR]

In this work, we develop a statistical framework for data clustering which uses hierarchical Dirichlet processes and Beta-Liouville distributions. The parameters of this framework are leaned using two variational Bayes approaches. The first one considers batch settings and the second one takes into account the dynamic nature of real data. Experimental results based on a challenging problem namely visual scenes categorization demonstrate the merits of the proposed framework. [ABSTRACT FROM AUTHOR]

There has been a constant desire for proposing new machine learning approaches for count data modeling. One of the most referred approaches is the latent Dirichlet allocation (LDA) model (Blei et al., 2003b). LDA has been shown to be a reliable model for count data classification. It is based, however, on the consideration of the Dirichlet distribution, as a prior, which modeling capabilities have been challenged recently and some alternative priors have been proposed. One of these priors is the Beta-Liouville (BL) distribution that we will consider in this work to provide an alternative to the LDA model. In order to maintain consistency with the original model we shall call our resulting model, latent Beta-Liouville allocation (LBLA). Like the LDA, the LBLA model uses a variational Bayes method for learning its hidden parameters. It will be shown that LDA is a special case of the LBLA model that we will show its merits, in comparison to the LDA model, via three distinct challenging applications namely text classification, natural scene categorization, and action recognition in videos. We will show that the LBLA model results in improved modeling accuracy in return for a slight increase in computational complexity. We conclude that our model can be considered as a more efficient replacement for the LDA model. [ABSTRACT FROM AUTHOR]

Parameter estimation is a cornerstone of most fundamental problems of statistical research and practice. In particular, finite mixture models have long been heavily relied on deterministic approaches such as expectation maximization (EM). Despite their successful utilization in wide spectrum of areas, they have inclined to converge to local solutions. An alternative approach is the adoption of Bayesian inference that naturally addresses data uncertainty while ensuring good generalization. To this end, in this paper we propose a fully Bayesian approach for Langevin mixture model estimation and selection via MCMC algorithm based on Gibbs sampler, Metropolis–Hastings and Bayes factors. We demonstrate the effectiveness and the merits of the proposed learning framework through synthetic data and challenging applications involving topic detection and tracking and image categorization. [ABSTRACT FROM AUTHOR]

The blending of generative and discriminative approaches has been prevailed by exploring and adopting distinct characteristic of each approach toward constructing a complementar system combining the best of both. The majority of current research in classification and categorization does not completely address the true structure and nature of data for particular application at hand. In contrast to most previous research, our proposed work focuses on the modeling and classification of spherical data that are naturally generated in many data mining and knowledge discovery applications such as text classification, visual scenes categorization and gene expression analysis. This paper investigates a generative mixture model to cluster spherical data based on Langevin distribution. In particular, we formulate a unified probabilistic framework, where we build probabilistic kernels based on Fisher score and information divergences from mixture of Langevin distributions for Support Vector Machine. We demonstrate the effectiveness and the merits of the proposed learning framework through synthetic data and challenging applications involving spam filtering using both textual and visual email contents. [ABSTRACT FROM AUTHOR]

We present a QoS-aware recommender approach based on probabilistic models to assist the selection of web services in open, distributed, and service-oriented environments. This approach allows consumers to maintain a trust model for each service provider they interact with, leading to the prediction of the most trustworthy service a consumer can interact with among a plethora of similar services. In this paper, we associate the trust in a service to its performance denoted by QoS ratings instigated by the amalgamation of various QoS metrics. Since the quality of a service is contingent, which renders its trustworthiness uncertain, we adopt a probabilistic approach for the prediction of the quality of a service based on the evaluation of past experiences (ratings) of each of its consumers. We represent the QoS ratings of services using different statistical distributions, namely multinomial Dirichlet, multinomial generalized Dirichlet, and multinomial Beta-Liouville. We leverage various machine learning techniques to compute the probabilities of each web service to belong to different quality classes. For instance, we use the Bayesian inference method to estimate the parameters of the aforementioned distributions, which presents a multidimensional probabilistic embodiment of the quality of the corresponding web services. We also employ a Bayesian network classifier with a Beta-Liouville prior to enable the classification of the QoS of composite services given the QoS of its constituents. We extend our approach to function in an online setting using the Voting EM algorithm that enables the estimation of the probabilities of the QoS after each interaction with a web service. Our experimental results demonstrate the effectiveness of the proposed approaches in modeling, classifying and incrementally learning the QoS ratings. [ABSTRACT FROM AUTHOR]

Abstract: Finite Dirichlet mixture models have proved to be an effective knowledge representation and inference engine in several machine learning and data mining applications. In this paper, we address the task of learning and selecting finite Dirichlet mixture models in an incremental variational way. A learning algorithm based on component splitting and local model selection is proposed. The merits of the proposed approach are illustrated using synthetic data as well as real challenging applications involving object detection, text documents clustering and distinguishing photographic images from computer graphics. [Copyright &y& Elsevier]

Learning appropriate statistical models is a fundamental data analysis task which has been the topic of continuing interest. Recently, finite Dirichlet mixture models have proved to be an effective and flexible model learning technique in several machine learning and data mining applications. In this article, the problem of learning and selecting finite Dirichlet mixture models is addressed using an expectation propagation (EP) inference framework. Within the proposed EP learning method, for finite mixture models, all the involved parameters and the model complexity (i.e. the number of mixture components), can be evaluated simultaneously in a single optimization framework. Extensive simulations using synthetic data along with two challenging real-world applications involving automatic image annotation and human action videos categorization demonstrate that our approach is able to achieve better results than comparable techniques. [ABSTRACT FROM AUTHOR]

Highlights: [•] A hierarchical statistical model for object clustering and recognition is proposed. [•] An algorithm is developed for the learning for the proposed hierarchical model. [•] Extensive experiments are conducted to validate the hierarchical model. [Copyright &y& Elsevier]

Highlights: [•] We develop a method for learning the parameters of multidimensional asymmetric generalized Gaussian mixtures (AGGM). [•] We describe and illustrate an online algorithm, based on the developed AGGM, for pedestrian detection using infrared images. [•] We implement a multiple-target tracking (MTT) framework using AGGM. [•] We demonstrate the importance of the fusion of both visible and thermal images for MTT. [ABSTRACT FROM AUTHOR]

We describe approaches for positive data modeling and classification using both finite inverted Dirichlet mixture models and support vector machines (SVMs). Inverted Dirichlet mixture models are used to tackle an outstanding challenge in SVMs namely the generation of accurate kernels. The kernels generation approaches, grounded on ideas from information theory that we consider, allow the incorporation of data structure and its structural constraints. Inverted Dirichlet mixture models are learned within a principled Bayesian framework using both Gibbs sampler and Metropolis-Hastings for parameter estimation and Bayes factor for model selection (i.e., determining the number of mixture's components). Our Bayesian learning approach uses priors, which we derive by showing that the inverted Dirichlet distribution belongs to the family of exponential distributions, over the model parameters, and then combines these priors with information from the data to build posterior distributions. We illustrate the merits and the effectiveness of the proposed method with two real-world challenging applications namely object detection and visual scenes analysis and classification. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a novel enhancement for unsupervised feature selection based on generalized Dirichlet (GD) mixture models. Our proposal is based on the extension of the finite mixture model previously developed in [1] to the infinite case, via the consideration of Dirichlet process mixtures, which can be viewed actually as a purely nonparametric model since the number of mixture components can increase as data are introduced. The infinite assumption is used to avoid problems related to model selection (i.e. determination of the number of clusters) and allows simultaneous separation of data in to similar clusters and selection of relevant features. Our resulting model is learned within a principled variational Bayesian framework that we have developed. The experimental results reported for both synthetic data and real-world challenging applications involving image categorization, automatic semantic annotation and retrieval show the ability of our approach to provide accurate models by distinguishing between relevant and irrelevant features without over- or under-fitting the data. [Copyright &y& Elsevier]

Abstract: Selecting relevant features in multidimensional data is important in several pattern analysis and image processing applications. The goal of this paper is to propose a Bayesian approach for identifying clusters of proportional data based on the selection of relevant features. More specifically, we consider the problem of selecting relevant features in unsupervised settings when generalized Dirichlet mixture models are considered to model and cluster proportional data. The learning of the proposed statistical model, to formulate the unsupervised feature selection problem, is carried out using a powerful reversible jump Markov chain Monte Carlo (RJMCMC) technique. Experiments involving the challenging problems of human action videos categorization, pedestrian detection and face recognition indicate that the proposed approach is efficient. [Copyright &y& Elsevier]

Abstract: Motivated by the high demand to construct compact and accurate statistical models that are automatically adjustable to dynamic changes, in this paper, we propose an online probabilistic framework for high-dimensional spherical data modeling. The proposed framework allows simultaneous clustering and feature selection in online settings using finite mixtures of von Mises distributions (movM). The unsupervised learning of the resulting model is approached using Expectation Maximization (EM) for parameter estimation along with minimum message length (MML) to determine the optimal number of mixture components. The gradient stochastic descent approach is considered for incremental updating of model parameters, also. Through empirical experiments, we demonstrate the merits of the proposed learning framework on diverse high dimensional datasets and challenging applications. [Copyright &y& Elsevier]

The work proposed in this paper is motivated by the need to develop powerful models and approaches to classify and learn proportional data. Indeed, an abundance of interesting data in several applications occur naturally in this form. Our goal is to discover and capture the intrinsic nature of the data by proposing some approaches that combine the major advantages of generative models namely finite mixtures and discriminative techniques namely support vector machines (SVMs). Indeed, SVMs often rely on classic kernels which are not generally meaningful for proportional data. One serious limitation of these kernels is that they do not take into account the nature of data to classify and choosing a suitable kernel continues to be a formidable challenge for data mining and machine learning researchers. Our approach builds on selecting accurate kernels generated from finite mixtures of Dirichlet, generalized Dirichlet and Beta-Liouville distributions which chief advantage is their flexibility and explanatory capabilities in the case of heterogenous proportional data. Using extensive simulations and a number of experiments involving scene modeling and classification, and automatic image orientation detection, we show the merits of the proposed mixture models and the accuracy of the generated kernels. [ABSTRACT FROM PUBLISHER]

Abstract: In this work we present an unsupervised algorithm for learning finite mixture models from multivariate positive data. Indeed, this kind of data appears naturally in many applications, yet it has not been adequately addressed in the past. This mixture model is based on the inverted Dirichlet distribution, which offers a good representation and modeling of positive non-Gaussian data. The proposed approach for estimating the parameters of an inverted Dirichlet mixture is based on the maximum likelihood (ML) using Newton Raphson method. We also develop an approach, based on the minimum message length (MML) criterion, to select the optimal number of clusters to represent the data using such a mixture. Experimental results are presented using artificial histograms and real data sets. The challenging problem of software modules classification is investigated within the proposed statistical framework, also. [Copyright &y& Elsevier]

Abstract: Recently hybrid generative discriminative approaches have emerged as an efficient knowledge representation and data classification engine. However, little attention has been devoted to the modeling and classification of non-Gaussian and especially proportional vectors. Our main goal, in this paper, is to discover the true structure of this kind of data by building probabilistic kernels from generative mixture models based on Liouville family, from which we develop the Beta-Liouville distribution, and which includes the well-known Dirichlet as a special case. The Beta-Liouville has a more general covariance structure than the Dirichlet which makes it more practical and useful. Our learning technique is based on a principled purely Bayesian approach which resulted models are used to generate support vector machine (SVM) probabilistic kernels based on information divergence. In particular, we show the existence of closed-form expressions of the Kullback–Leibler and Rényi divergences between two Beta-Liouville distributions and then between two Dirichlet distributions as a special case. Through extensive simulations and a number of experiments involving synthetic data, visual scenes and texture images classification, we demonstrate the effectiveness of the proposed approaches. [Copyright &y& Elsevier]

Electronic mail is a major revolution taking place over traditional communication systems due to its convenient, economical, fast, and easy to use nature. A major bottleneck in electronic communications is the enormous dissemination of unwanted, harmful emails known as spam emails. A major concern is the developing of suitable filters that can adequately capture those emails and achieve high performance rate. Machine learning (ML) researchers have developed many approaches in order to tackle this problem. Within the context of machine learning, support vector machines (SVM) have made a large contribution to the development of spam email filtering. Based on SVM, different schemes have been proposed through text classification approaches (TC). A crucial problem when using SVM is the choice of kernels as they directly affect the separation of emails in the feature space. This paper presents thorough investigation of several distance-based kernels and specify spam filtering behaviors using SVM. The majority of used kernels in recent studies concern continuous data and neglect the structure of the text. In contrast to classical kernels, we propose the use of various string kernels for spam filtering. We show how effectively string kernels suit spam filtering problem. On the other hand, data preprocessing is a vital part of text classification where the objective is to generate feature vectors usable by SVM kernels. We detail a feature mapping variants in TC that yield improved performance for the standard SVM in filtering task. Furthermore, to cope for realtime scenarios we propose an online active framework for spam filtering. We present empirical results from an extensive study of online, transductive, and online active methods for classifying spam emails in real time. We show that active online method using string kernels achieves higher precision and recall rates. [ABSTRACT FROM AUTHOR]

Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture model-based approach for proportional vector clustering is proposed. In particular, a sophisticated entropy-based variational algorithm is developed to learn the model and optimize its complexity simultaneously. Moreover, a component-splitting principle is investigated, here, to handle the problem of model selection and to prevent over-fitting, which is an added advantage, as it is done within the variational framework. The performance and merits of the proposed framework are evaluated on multiple, real-challenging applications including dynamic textures clustering, objects categorization and human gesture recognition. [ABSTRACT FROM AUTHOR]

The accurate detection of abnormalities in medical images (like X-ray and CT scans) is a challenging problem due to images' blurred boundary contours, different sizes, variable shapes, and uneven density. In this paper, we tackle this problem via a new effective online variational learning model for both mixtures of finite and infinite Gamma distributions. The proposed approach takes advantage of the Gamma distribution flexibility, the online learning scalability, and the variational inference efficiency. Three different batch and online learning methods based on robust texture-based feature extraction are proposed. Our work is evaluated and validated on several real challenging data sets for different kinds of pneumonia infection detection. The obtained results are very promising given that we approach the classification problem in an unsupervised manner. They also confirm the superiority of the Gamma mixture model compared to the Gaussian mixture model for medical images' classification. [ABSTRACT FROM AUTHOR]

Highlights: [•] A finite mixture model based on the generalized inverted Dirichlet distribution is proposed. [•] An approach to learn the new proposed mixture model is developed. [•] The proposed statistical framework is applied to object classification and forgery detection. [ABSTRACT FROM AUTHOR]

• A novel approach to assess the quality of training data has been proposed, in particular, two scores (Sscore and Qscore) are calculated depending on a new concept, called spread rate. • Spread rate is successfully applied to the occupancy estimation application via an interactive learning approach. • Interactive learning is an extension of a supervised learning machine that in our case will estimate the occupancy by collecting the required labeling from the occupants themselves. • The question of when asking to occupants is investigated due to the database quality. • Spread rate is compared with the density of the neighborhood, while the spread rate replaces the density of the neighborhood. It moves from a local criterion to a global one (instead of counting the records, it checks how records are globally distributed). Data quality assesment is a key component for many real applications, since it can drive better modelling. In this work a methodology to asses data quality (Qscore) is proposed and discussed. The validation of Qscore is performed via an interactive learning experiment related to occupancy estimation. Interactive learning has been shown to be crucial to consider and integrate occupant behavior in smart buildings. Indeed, valuable feedback and information can be collected from the occupants by involving them and by improving their consciousness about energy management systems. Users should feel involved to keep developing highly energy-efficient buildings. To reach this goal, occupants should be aware of the building features to feel more in control. This paper proposes a framework to interact with occupants to estimate building occupancy. This framework is based on an enhanced supervised learning approach that involves interaction with occupants, when necessary, to keep collecting training data. The training data consist of the measurements (i.e. features) collected from common sensors, for instance, motion detection, power consumption, and CO 2 concentration, and the label (i.e. number of occupants) provided by the occupants during interactions. The considered learning machine in our experiments is the Multi-layer Perceptron regressor (MLP), although other approaches could be easily integrated within the proposed framework. In order to avoid useless interaction with users a new concept is introduced, called spread rate, to measure the quality of the data to decide if an interaction with the user is necessary or not. Extensive simulations have shown the merits of the proposed approach. [ABSTRACT FROM AUTHOR]

Machine learning (ML), a branch of artificial intelligence (AI), is an area of computational science that is concerned with the analysis and interpretation of patterns and structures in data to enable learning and decision-making without the participation of a human. Hidden Markov models (HMMs), which have been acknowledged for decades but have recently made a significant revival in machine learning, are one of the most impressively powerful probabilistic models. HMMs are frequently employed in machine learning to model heterogeneous time series data. In this paper, we integrate independent component analysis (ICA) and ICA with a bounded multivariate generalized Gaussian mixture model (ICA-BMGGMM) into the HMM approach. One limitation of ICA is that it assumes the sources to be independent from each other. This assumption can be relaxed by combining independent vectors analysis (IVA) and IVA with the BMGGMM (IVA-BMGGMM) into the HMM approach to improve their modeling capability. We validate our proposed models using a variety of applications, such as human action recognition, speech recognition, and energy disaggregation. The results presented in the paper demonstrate the effectiveness of the proposed approaches for modeling different types of data. These data include KTH and Weizmann datasets for human action recognition, TIMIT and SDR for speech recognition, REDD dataset for energy disaggregation and EEG dataset for elliptic seizure classification. For all conducted experiments, our proposed models outperform other comparing models for all performance metrics such as accuracy, sensitivity, and precision. The best detection results were found using the IVABMGGMM-HMM for the reported experiments. [ABSTRACT FROM AUTHOR]