11 Result(s)
within
Wentao Fan
Complex Systems
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Article
Unsupervised image categorization based on deep generative models with disentangled representations and von Mises-Fisher distributions
Variational autoencoders (VAEs) have emerged as powerful deep generative models for learning abstract representations in the latent space, making them highly applicable across diverse domains. This paper prese...
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Article
Deep generative clustering methods based on disentangled representations and augmented data
This paper presents a novel clustering approach that utilizes variational autoencoders (VAEs) with disentangled representations, enhancing the efficiency and effectiveness of clustering. Traditional VAE-based ...
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Article
Transformer-based contrastive learning framework for image anomaly detection
Anomaly detection refers to the problem of uncovering patterns in a given data set that do not conform to the expected behavior. Recently, owing to the continuous development of deep representation learning, a...
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Article
Unsupervised modeling and feature selection of sequential spherical data through nonparametric hidden Markov models
As spherical data (i.e. \(L_2\) L 2 ...
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Article
Unsupervised Video Object Segmentation Based on Mixture Models and Saliency Detection
In this paper, we propose an unsupervised video object segmentation approach which is mainly based on a saliency detection method and the Gaussian mixture model with Markov random field. In our approach, the s...
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Article
Simultaneous clustering and feature selection via nonparametric Pitman–Yor process mixture models
Mixture models constitute one of the most important machine learning approaches. Indeed, they can be considered as the workhorse of generative machine learning. The majority of existing works consider mixtures...
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Article
Motives and periods in Bianchi IX gravity models
We show that, when considering the anisotropic scaling factors and their derivatives as affine variables, the coefficients of the heat-kernel expansion of the Dirac–Laplacian on SU(2) Bianchi IX metrics are algeb...
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Article
A Novel Model-Based Approach for Medical Image Segmentation Using Spatially Constrained Inverted Dirichlet Mixture Models
In this paper, we present a novel statistical approach to medical image segmentation. This approach is based on finite mixture models with spatial smoothness constrains. The main advantages of the proposed app...
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Article
Model-Based Clustering Based on Variational Learning of Hierarchical Infinite Beta-Liouville Mixture Models
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 variatio...
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Article
Non-Gaussian Data Clustering via Expectation Propagation Learning of Finite Dirichlet Mixture Models and Applications
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 flexibl...
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Article
Online variational learning of finite Dirichlet mixture models
In this paper, we present an online variational inference algorithm for finite Dirichlet mixture models learning. Online algorithms allow data points to be processed one at a time, which is important for real-...
