The statistical model for community detection is a promising research area in network analysis.Most existing statistical models of community detection are designed for networks with a known type of community structure...The statistical model for community detection is a promising research area in network analysis.Most existing statistical models of community detection are designed for networks with a known type of community structure,but in many practical situations,the types of community structures are unknown.To cope with unknown community structures,diverse types should be considered in one model.We propose a model that incorporates the latent interaction pattern,which is regarded as the basis of constructions of diverse community structures by us.The interaction pattern can parameterize various types of community structures in one model.A collapsed Gibbs sampling inference is proposed to estimate the community assignments and other hyper-parameters.With the Pitman-Yor process as a prior,our model can automatically detect the numbers and sizes of communities without a known type of community structure beforehand.Via Bayesian inference,our model can detect some hidden interaction patterns that offer extra information for network analysis.Experiments on networks with diverse community structures demonstrate that our model outperforms four state-of-the-art models.展开更多
Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications.In particular,recognizin...Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications.In particular,recognizing human activities with accurate results have become a topic of high interest.Although the current tools have reached remarkable successes,it is still a challenging problem due to various uncontrolled environments and conditions.In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some realworld applications.In particular,two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed.These models are then applied to address the problem of modelling grouped data where observations are organized into groups and these groups are statistically linked by sharing mixture components.The choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed distributions.In addition,deploying the Dirichlet process prior is justified by its advantage of automatically finding the right number of components and providing nice properties.Moreover,a learning step via variational Bayesian setting is presented in a flexible way.The priors over the parameters are selected appropriately and the posteriors are approximated effectively in a closed form.Experimental results based on a real-life applications that concerns texture classification and human actions recognition show the capabilities and effectiveness of the proposed framework.展开更多
基金Project supported by Beijing Natural Science Foundation,China(Grant Nos.L181010 and 4172054)the National Key R&D Program of China(Grant No.2016YFB0801100)the National Basic Research Program of China(Grant No.2013CB329605)。
文摘The statistical model for community detection is a promising research area in network analysis.Most existing statistical models of community detection are designed for networks with a known type of community structure,but in many practical situations,the types of community structures are unknown.To cope with unknown community structures,diverse types should be considered in one model.We propose a model that incorporates the latent interaction pattern,which is regarded as the basis of constructions of diverse community structures by us.The interaction pattern can parameterize various types of community structures in one model.A collapsed Gibbs sampling inference is proposed to estimate the community assignments and other hyper-parameters.With the Pitman-Yor process as a prior,our model can automatically detect the numbers and sizes of communities without a known type of community structure beforehand.Via Bayesian inference,our model can detect some hidden interaction patterns that offer extra information for network analysis.Experiments on networks with diverse community structures demonstrate that our model outperforms four state-of-the-art models.
基金The authors would like to thank Taif University Researchers Supporting Project number(TURSP-2020/26),Taif University,Taif,Saudi ArabiaThey would like also to thank Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R40),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications.In particular,recognizing human activities with accurate results have become a topic of high interest.Although the current tools have reached remarkable successes,it is still a challenging problem due to various uncontrolled environments and conditions.In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some realworld applications.In particular,two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed.These models are then applied to address the problem of modelling grouped data where observations are organized into groups and these groups are statistically linked by sharing mixture components.The choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed distributions.In addition,deploying the Dirichlet process prior is justified by its advantage of automatically finding the right number of components and providing nice properties.Moreover,a learning step via variational Bayesian setting is presented in a flexible way.The priors over the parameters are selected appropriately and the posteriors are approximated effectively in a closed form.Experimental results based on a real-life applications that concerns texture classification and human actions recognition show the capabilities and effectiveness of the proposed framework.
