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On Choosing a Mixture Model for Clustering [PDF]
Journal of Data Science, 2021 2 Universit e de Caen Abstract: Two methods for clustering data and choosing a mixture model are proposed. First, we derive a new classication algorithm based on the classication likelihood. Then, the likelihood conditional on these clusters is written as the product of likelihoods of each cluster, and AIC- respectively BIC-type approximations are ...
Ngatchou Wandji, Joseph, Bulla, Jan
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Graph Laplacian Mixture Model [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2020 Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph.
Hermina Petric Maretic, Pascal Frossard
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A family of mixture models for biclustering [PDF]
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractBiclustering is used for simultaneous clustering of the observations and variables when there is no group structure known a priori. It is being increasingly used in bioinformatics, text analytics, and so on. Previously, biclustering has been introduced in a model‐based clustering framework by utilizing a structure similar to a mixture of factor
Wangshu Tu, Sanjeena Subedi
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Deep Gaussian mixture models [PDF]
Statistics and Computing, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables ...
Cinzia Viroli, Geoffrey J. McLachlan
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On a Mixture Autoregressive Model
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2000 Summary We generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mixture of K stationary or non-stationary AR components. The advantages of the MAR model over the GMTD model include
Wong, CS, Li, WK
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, 2019 Mixtures of experts models provide a framework in which covariates may be included in mixture models. This is achieved by modelling the parameters of the mixture model as functions of the concomitant covariates. Given their mixture model foundation, mixtures of experts models possess a diverse range of analytic uses, from clustering observations to ...
Gormley, Isobel Claire +1 more
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Geodesic Finite Mixture Models [PDF]
Proceedings of the British Machine Vision Conference 2014, 2014 Peer ...
Simó Serra, Edgar +2 more
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CoRR, 2016 Casting neural networks in generative frameworks is a highly sought-after endeavor these days. Contemporary methods, such as Generative Adversarial Networks, capture some of the generative capabilities, but not all. In particular, they lack the ability of tractable marginalization, and thus are not suitable for many tasks.
Or Sharir +3 more
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Mixture-model adaptation for SMT [PDF]
Proceedings of the Second Workshop on Statistical Machine Translation - StatMT '07, 2007 We describe a mixture-model approach to adapting a Statistical Machine Translation System for new domains, using weights that depend on text distances to mixture components. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and translation model adaptation;
Foster, George, Kuhn, Roland
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Mixture model averaging for clustering [PDF]
Advances in Data Analysis and Classification, 2014 In mixture model-based clustering applications, it is common to fit several models from a family and report clustering results from only the `best' one. In such circumstances, selection of this best model is achieved using a model selection criterion, most often the Bayesian information criterion.
Yuhong Wei, Paul D. McNicholas
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