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Unsupervised nested Dirichlet finite mixture model for clustering
The Dirichlet distribution is widely used in the context of mixture models. Despite its flexibility, it still suffers from some limitations, such as...
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Deep graph clustering via mutual information maximization and mixture model
Attributed graph clustering or community detection which learns to cluster the nodes of a graph is a challenging task in graph analysis. Recently...
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A similarity-based Bayesian mixture-of-experts model
We present a new nonparametric mixture-of-experts model for multivariate regression problems, inspired by the probabilistic k -nearest neighbors...
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Finite Libby-Novick Beta Mixture Model: An MML-Based Approach
We propose an unsupervised algorithm for learning the optimal number of clusters in a finite Libby-Novick Beta mixture model. In unsupervised...
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Generalized labeled multi-Bernoulli filter with signal features of unknown emitters
A novel algorithm that combines the generalized labeled multi-Bernoulli (GLMB) filter with signal features of the unknown emitter is proposed in this...
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Integrating semantic similarity with Dirichlet multinomial mixture model for enhanced web service clustering
With accelerated advancement of web 2.0, developers generally describe the functionality of services in short natural text. Keyword-based searching...
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Bernoulli at the Root of Horizontal Side Channel Attacks
Vertical side-channel attacks represent a major threat to the confidentiality of enclosed secrets in hardware devices. Fortunately, countermeasures...
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Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems
In this study, we extend traditional (single-target) hybrid systems to multi-target hybrid systems with a focus on the multi-maneuvering-target...
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MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models
Imputing missing data is still a challenge for mixed datasets containing variables of different nature such as continuous, count, ordinal,...
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Heterogeneous analysis for clustered data using grouped finite mixture models
It is common to observe significant heterogeneity in clustered data across scientific fields. Cluster-wise conditional distributions are widely used...
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Topic Modeling for Short Texts via Adaptive P \(\acute{o}\) lya Urn Dirichlet Multinomial Mixture
Inferring coherent and diverse latent topics from short texts is crucial in topic modeling. Existing approaches leverage the Generalized P...
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MIAMI: MIxed Data Augmentation MIxture
Performing data augmentation for mixed datasets remains an open challenge. We propose an adaptation of the Mixed Deep Gaussian Mixture Models (MDGMM)...
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Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series
The classification of irregularly sampled Satellite image time-series (SITS) is investigated in this paper. A multivariate Gaussian process mixture...
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Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space
This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding...
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Latent Variable Model Selection
Latent variable models are important knowledge representations for machine learning. This chapter introduces two information-theoretic criteria for...
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Bayesian sparse regularization for parallel MRI reconstruction using complex Bernoulli–Laplace mixture priors
Parallel imaging technique using several receiver coils provides a fast acquisition of magnetic resonance imaging (MRI) images with high temporal...
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Communication-Efficient Model Fusion
We consider the problem of learning a federated model where the number of communication rounds is severely limited. We discuss recent works on model...
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Latent Block Regression Model
When dealing with high dimensional sparse data, such as in recommender systems,co-clusteringturnsouttobemorebeneficialthanone-sidedclustering,even if...
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Model-based clustering of multiple networks with a hierarchical algorithm
The paper tackles the problem of clustering multiple networks, directed or not, that do not share the same set of vertices, into groups of networks...
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Mixture-Based Unsupervised Learning for Positively Correlated Count Data
The Multinomial distribution has been widely used to model count data. However, its Naive Bayes assumption usually degrades clustering performance...