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Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings

Author(s): Guo J; Fan W; Amayri M; Bouguila N;

This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering methods use the Gaussian mixture model (GMM) as a prior on the latent space. We employ a more flexible asy ...

Article GUID: 39662201


FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images

Author(s): Paul S; Patterson Z; Bouguila N;

The application of large field-of-view (FoV) cameras equipped with fish-eye lenses brings notable advantages to various real-world computer vision applications, including autonomous driving. While deep learning has proven successful in conventional computer vision applications using regular perspective images, its potential in fish-eye camera contexts rem ...

Article GUID: 38535151


Perceptions of self-monitoring dietary intake according to a plate-based approach: A qualitative study

Author(s): Kheirmandparizi M; Gouin JP; Bouchaud CC; Kebbe M; Bergeron C; Madani Civi R; Rhodes RE; Farnesi BC; Bouguila N; Conklin AI; Lear SA; Cohen TR;

Dietary self-monitoring is a behaviour change technique used to help elicit and sustain dietary changes over time. Current dietary self-monitoring tools focus primarily on itemizing foods and counting calories, which can be complex, time-intensive, and dependent on health literacy. Further, there ...

Article GUID: 38015899


Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency

Author(s): Al-Bazzaz H; Azam M; Amayri M; Bouguila N;

Smart meter datasets have recently transitioned from monthly intervals to one-second granularity, yielding invaluable insights for diverse metering functions. Clustering analysis, a fundamental data mining technique, is extensively applied to discern unique energy consumption patterns. However, the advent of high-resolution smart meter data brings forth f ...

Article GUID: 37837127


Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration

Author(s): Ge B; Najar F; Bouguila N;

In this paper, a weighted multivariate generalized Gaussian mixture model combined with stochastic optimization is proposed for point cloud registration. The mixture model parameters of the target scene and the scene to be registered are updated iteratively by the fixed point method under the framework of the EM algorithm, and the number of components is ...

Article GUID: 37754943


Human Activity Recognition with an HMM-Based Generative Model

Author(s): Manouchehri N; Bouguila N;

Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scale ...

Article GUID: 36772428


Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications

Author(s): Luo Z; Amayri M; Fan W; Bouguila N;

Cross-collection topic models extend previous single-collection topic models, such as Latent Dirichlet Allocation (LDA), to multiple collections. The purpose of cross-collection topic modeling is to model document-topic representations and reveal similarities between each topic and differences among groups. However, the restriction of Dirichlet prior and ...

Article GUID: 36685642


Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning

Author(s): Bouhamed O; Amayri M; Bouguila N;

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 insp ...

Article GUID: 35590880


Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures

Author(s): Bourouis S; Pawar Y; Bouguila N;

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 sophi ...

Article GUID: 35009726


Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Author(s): Bourouis S; Alharbi A; Bouguila N;

Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shap ...

Article GUID: 34460578


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