Deep Gaussian mixture models
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 (DGMM) are introduced and discussed. A DGMM is a network of multiple layers of latent variables, where...
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| Veröffentlicht in: | Statistics and computing Jg. 29; H. 1; S. 43 - 51 |
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| Abstract | 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 (DGMM) are introduced and discussed. A DGMM is a network of multiple layers of latent variables, where, at each layer, the variables follow a mixture of Gaussian distributions. Thus, the deep mixture model consists of a set of nested mixtures of linear models, which globally provide a nonlinear model able to describe the data in a very flexible way. In order to avoid overparameterized solutions, dimension reduction by factor models can be applied at each layer of the architecture, thus resulting in deep mixtures of factor analyzers. |
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| AbstractList | 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 (DGMM) are introduced and discussed. A DGMM is a network of multiple layers of latent variables, where, at each layer, the variables follow a mixture of Gaussian distributions. Thus, the deep mixture model consists of a set of nested mixtures of linear models, which globally provide a nonlinear model able to describe the data in a very flexible way. In order to avoid overparameterized solutions, dimension reduction by factor models can be applied at each layer of the architecture, thus resulting in deep mixtures of factor analyzers. |
| Author | McLachlan, Geoffrey J. Viroli, Cinzia |
| Author_xml | – sequence: 1 givenname: Cinzia orcidid: 0000-0002-3278-5266 surname: Viroli fullname: Viroli, Cinzia email: cinzia.viroli@unibo.it organization: Department of Statistical Sciences, University of Bologna – sequence: 2 givenname: Geoffrey J. surname: McLachlan fullname: McLachlan, Geoffrey J. organization: Department of Mathematics, University of Queensland |
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| Cites_doi | 10.1177/1471082X0901000405 10.1038/nature14539 10.1080/01621459.1990.10474930 10.1109/TPAMI.2009.149 10.1016/S0167-9473(02)00183-4 10.1198/jcgs.2010.08111 10.1007/s11634-010-0058-3 10.1002/0471721182 10.1198/106186005X59586 10.1198/016214502760047131 10.1016/j.neunet.2014.09.003 10.1007/s00357-010-9063-7 10.1080/10618600.2014.978007 10.1109/DICTA.2009.88 10.32614/RJ-2016-021 10.1109/CVPR.2014.220 |
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| References | Viroli (CR20) 2010; 27 Scrucca, Fop, Murphy, Raftery (CR16) 2016; 8 Montanari, Viroli (CR14) 2010; 10 LeCun, Bengio, Hinton (CR8) 2015; 521 McLachlan, Peel (CR12) 2000 CR19 McLachlan, Peel, Bean (CR11) 2003; 41 CR18 CR17 Wei, Tanner (CR22) 1990; 85 Forina, Tiscornia (CR5) 1982; 72 Baudry, Raftery, Celeux, Lo, Gottardo (CR2) 2010; 19 CR21 Fraley, Raftery (CR6) 2002; 97 Schmidhuber (CR15) 2015; 61 Hennig (CR7) 2010; 4 Celeux, Diebolt (CR3) 1985; 2 Forina, Armanino, Castino, Ubigli (CR4) 1986; 25 Baek, McLachlan, Flack (CR1) 2010; 32 Li (CR9) 2005; 14 Mardia, Kent, Bibby (CR10) 1976 Melnykov (CR13) 2016; 25 A Montanari (9793_CR14) 2010; 10 KV Mardia (9793_CR10) 1976 G McLachlan (9793_CR11) 2003; 41 J Schmidhuber (9793_CR15) 2015; 61 GJ McLachlan (9793_CR12) 2000 C Viroli (9793_CR20) 2010; 27 GC Wei (9793_CR22) 1990; 85 J Baek (9793_CR1) 2010; 32 M Forina (9793_CR5) 1982; 72 J Li (9793_CR9) 2005; 14 Y LeCun (9793_CR8) 2015; 521 V Melnykov (9793_CR13) 2016; 25 M Forina (9793_CR4) 1986; 25 L Scrucca (9793_CR16) 2016; 8 G Celeux (9793_CR3) 1985; 2 C Hennig (9793_CR7) 2010; 4 9793_CR21 9793_CR19 J-P Baudry (9793_CR2) 2010; 19 9793_CR17 C Fraley (9793_CR6) 2002; 97 9793_CR18 |
| References_xml | – volume: 10 start-page: 441 issue: 4 year: 2010 end-page: 460 ident: CR14 article-title: Heteroscedastic factor mixture analysis publication-title: Stat. Model. doi: 10.1177/1471082X0901000405 – ident: CR21 – volume: 521 start-page: 436 issue: 7553 year: 2015 end-page: 444 ident: CR8 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 85 start-page: 699 issue: 411 year: 1990 end-page: 704 ident: CR22 article-title: A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1990.10474930 – volume: 32 start-page: 1298 issue: 7 year: 2010 end-page: 1309 ident: CR1 article-title: Mixtures of factor analyzers with common factor loadings: applications to the clustering and visualization of high-dimensional data publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2009.149 – ident: CR19 – ident: CR18 – ident: CR17 – volume: 41 start-page: 379 issue: 3 year: 2003 end-page: 388 ident: CR11 article-title: Modelling high-dimensional data by mixtures of factor analyzers publication-title: Comput. Stat. Data Anal. doi: 10.1016/S0167-9473(02)00183-4 – volume: 19 start-page: 332 issue: 2 year: 2010 end-page: 353 ident: CR2 article-title: Combining mixture components for clustering publication-title: J. Comput. Gr. Stat. doi: 10.1198/jcgs.2010.08111 – volume: 4 start-page: 3 issue: 1 year: 2010 end-page: 34 ident: CR7 article-title: Methods for merging gaussian mixture components publication-title: Adv. Data Anal. Classif. doi: 10.1007/s11634-010-0058-3 – year: 2000 ident: CR12 publication-title: Finite Mixture Models doi: 10.1002/0471721182 – volume: 14 start-page: 547 issue: 3 year: 2005 end-page: 568 ident: CR9 article-title: Clustering based on a multilayer mixture model publication-title: J. Comput. Gr. Stat. doi: 10.1198/106186005X59586 – volume: 97 start-page: 611 year: 2002 end-page: 631 ident: CR6 article-title: Model-based clustering, discriminant analysis and density estimation publication-title: J. Am. Stat. Assoc. doi: 10.1198/016214502760047131 – volume: 25 start-page: 189 issue: 3 year: 1986 end-page: 201 ident: CR4 article-title: Multivariate data analysis as a discriminating method of the origin of wines publication-title: Vitis – volume: 61 start-page: 85 year: 2015 end-page: 117 ident: CR15 article-title: Deep learning in neural networks: an overview publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.09.003 – volume: 2 start-page: 73 issue: 1 year: 1985 end-page: 82 ident: CR3 article-title: The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem publication-title: Comput. Stat. Q. – volume: 27 start-page: 363 issue: 3 year: 2010 end-page: 388 ident: CR20 article-title: Dimensionally reduced model-based clustering through mixtures of factor mixture analyzers publication-title: J. Classif. doi: 10.1007/s00357-010-9063-7 – volume: 72 start-page: 143 issue: 3–4 year: 1982 end-page: 155 ident: CR5 article-title: Pattern-recognition methods in the prediction of Italian olive oil origin by their fatty-acid content publication-title: Anal. Chim. – volume: 25 start-page: 66 issue: 1 year: 2016 end-page: 90 ident: CR13 article-title: Merging mixture components for clustering through pairwise overlap publication-title: J. Comput. Gr. Stat. doi: 10.1080/10618600.2014.978007 – volume: 8 start-page: 289 year: 2016 end-page: 317 ident: CR16 article-title: mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models publication-title: R Journal – year: 1976 ident: CR10 publication-title: Multivariate Analysis – volume-title: Multivariate Analysis year: 1976 ident: 9793_CR10 – volume: 10 start-page: 441 issue: 4 year: 2010 ident: 9793_CR14 publication-title: Stat. Model. doi: 10.1177/1471082X0901000405 – volume: 27 start-page: 363 issue: 3 year: 2010 ident: 9793_CR20 publication-title: J. Classif. doi: 10.1007/s00357-010-9063-7 – volume: 25 start-page: 66 issue: 1 year: 2016 ident: 9793_CR13 publication-title: J. Comput. Gr. Stat. doi: 10.1080/10618600.2014.978007 – ident: 9793_CR17 – volume: 61 start-page: 85 year: 2015 ident: 9793_CR15 publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.09.003 – volume: 19 start-page: 332 issue: 2 year: 2010 ident: 9793_CR2 publication-title: J. Comput. Gr. Stat. doi: 10.1198/jcgs.2010.08111 – ident: 9793_CR19 – volume: 2 start-page: 73 issue: 1 year: 1985 ident: 9793_CR3 publication-title: Comput. Stat. Q. – volume-title: Finite Mixture Models year: 2000 ident: 9793_CR12 doi: 10.1002/0471721182 – volume: 14 start-page: 547 issue: 3 year: 2005 ident: 9793_CR9 publication-title: J. Comput. Gr. Stat. doi: 10.1198/106186005X59586 – volume: 97 start-page: 611 year: 2002 ident: 9793_CR6 publication-title: J. Am. Stat. Assoc. doi: 10.1198/016214502760047131 – ident: 9793_CR21 doi: 10.1109/DICTA.2009.88 – volume: 8 start-page: 289 year: 2016 ident: 9793_CR16 publication-title: R Journal doi: 10.32614/RJ-2016-021 – volume: 72 start-page: 143 issue: 3–4 year: 1982 ident: 9793_CR5 publication-title: Anal. Chim. – volume: 25 start-page: 189 issue: 3 year: 1986 ident: 9793_CR4 publication-title: Vitis – volume: 32 start-page: 1298 issue: 7 year: 2010 ident: 9793_CR1 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2009.149 – volume: 4 start-page: 3 issue: 1 year: 2010 ident: 9793_CR7 publication-title: Adv. Data Anal. Classif. doi: 10.1007/s11634-010-0058-3 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 9793_CR8 publication-title: Nature doi: 10.1038/nature14539 – volume: 85 start-page: 699 issue: 411 year: 1990 ident: 9793_CR22 publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1990.10474930 – volume: 41 start-page: 379 issue: 3 year: 2003 ident: 9793_CR11 publication-title: Comput. Stat. Data Anal. doi: 10.1016/S0167-9473(02)00183-4 – ident: 9793_CR18 doi: 10.1109/CVPR.2014.220 |
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