Results 31 to 40 of about 23,712,988 (293)
Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease [PDF]
, 2012 In this paper, the goal of identifying disease subgroups based on differences in observed symptom profile is considered. Commonly referred to as phenotype identification, solutions to this task often involve the application of unsupervised clustering ...
Johnson, Helen +2 more
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A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency
Entropy, 2018 Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge.
Xulun Ye, Jieyu Zhao, Yu Chen
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IEEE Access, 2022 Due to the unique channel characteristics of Terahertz (THz), comprehensive propagation channel modeling is essential to understand the spectrum and develop reliable communication systems in these bands.
Erhan Karakoca +2 more
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Generative Supervised Classification Using Dirichlet Process Priors. [PDF]
, 2010 Choosing the appropriate parameter prior distributions associated to a given Bayesian model is a challenging problem. Conjugate priors can be selected for simplicity motivations.
Davy, Manuel +3 more
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Extensible Gaussian Mixture Model for Image Prior Modeling [PDF]
Jisuanji gongcheng, 2020 To address the inextensible fixed number of components in image prior modeling based on Gaussian Mixture Model(GMM),this paper proposes an extensible GMM model based on Dirichlet Process(DP).Through the addition and merging mechanism of cluster ...
ZHANG Mohua, PENG Jianhua
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Dirichlet Process Mixture Models for the Analysis of Repeated Attempt Designs
Biometrics, 2023 AbstractIn longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful information for the purposes of assessing missing data assumptions.
Michael J. Daniels, Minji Lee, Wei Feng
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Energies, 2020 Traditional robust optimization methods use box uncertainty sets or gamma uncertainty sets to describe wind power uncertainty. However, these uncertainty sets fail to utilize wind forecast error probability information and assume that the wind forecast ...
Li Dai, Dahai You, Xianggen Yin
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IET Image Processing, 2020 Mixture models are broadly applied in image processing domains. Related existing challenges include failure to approximate exact data shapes, estimate correct number of components, and ignore irrelevant features.
Ziyang Song, Samr Ali, Nizar Bouguila
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Nonparametric empirical Bayes for the Dirichlet process mixture model [PDF]
Statistics and Computing, 2006 The Dirichlet process prior allows flexible nonparametric mixture modeling. The number of mixture components is not specified in advance and can grow as new data arrive. However, analyses based on the Dirichlet process prior are sensitive to the choice of the parameters, including an infinite-dimensional distributional parameter G 0.
Jon D. McAuliffe +2 more
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Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography
2022 25th International Conference on Information Fusion (FUSION), 2022 Numerous sleep disorders are characterised by movement during sleep, these include rapid-eye movement sleep behaviour disorder (RBD) and periodic limb movement disorder. The process of diagnosing movement related sleep disorders requires laborious and time-consuming visual analysis of sleep recordings.
Cooray, N. +8 more
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