Wang, Pu and Domeniconi, Carlotta and Laskey, Kathryn
Latent Dirichlet Bayesian Co-Clustering
Machine Learning and Knowledge Discovery in Databases
2009
522–537
Co-clustering has emerged as an important technique for mining contingency data matrices. However, almost all existing co-clustering
algorithms are hard partitioning, assigning each row and column of the data matrix to one cluster. Recently a Bayesian co-clusteringapproach has been proposed which allows a probability distribution membership in row and column clusters. The approach usesvariational inference for parameter estimation. In this work, we modify the Bayesian co-clustering model, and use collapsedGibbs sampling and collapsed variational inference for parameter estimation. Our empirical evaluation on real data sets showsthat both collapsed Gibbs sampling and collapsed variational inference are able to find more accurate likelihood estimatesthan the standard variational Bayesian co-clustering approach.
http://dx.doi.org/10.1007/978-3-642-04174-7_34
2009, bayesian, clustering, ecml, lda, pkdd
Shan, Hanhuai and Banerjee, Arindam
Bayesian Co-clustering.
ICDM
IEEE Computer Society
2008
530-539
http://dblp.uni-trier.de/db/conf/icdm/icdm2008.html#ShanB08
conf/icdm/2008
bayesian, clustering, co-clustering, lda
Teh, Y. W. and Kurihara, K. and Welling, M.
Collapsed Variational Inference for HDP
Advances in Neural Information Processing Systems
2008
20
bayesian, collapsed, hierachical, inference, lda, variational
Vazquez, Alexei
Bayesian approach to clustering real value, categorical and network
data: solution via variational methods
2008
Data clustering, including problems such as finding network communities, can
be put into a systematic framework by means of a Bayesian approach. The
application of Bayesian approaches to real problems can be, however, quite
challenging. In most cases the solution is explored via Monte Carlo sampling or
variational methods. Here we work further on the application of variational
methods to clustering problems. We introduce generative models based on a
hidden group structure and prior distributions. We extend previous attends by
Jaynes, and derive the prior distributions based on symmetry arguments. As a
case study we address the problems of two-sides clustering real value data and
clustering data represented by a hypergraph or bipartite graph. From the
variational calculations, and depending on the starting statistical model for
the data, we derive a variational Bayes algorithm, a generalized version of the
expectation maximization algorithm with a built in penalization for model
complexity or bias. We demonstrate the good performance of the variational
Bayes algorithm using test examples.
http://arxiv.org/abs/0805.2689
bayesian, clustering, community, detection
cite arxiv:0805.2689
Comment: 12 pages, 5 figures. New sections added
Teh, Y.W. and Jordan, M.I. and Beal, M.J. and Blei, D.M.
Hierarchical dirichlet processes
Journal of the American Statistical Association
Citeseer
2006
101
1566–1581
476
http://scholar.google.de/scholar.bib?q=info:NVEeNb3JVywJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0
bayesian, dirichlet, gibbs, hierarchical, lda
Banerjee, Arindam and Merugu, Srujana and Dhillon, Inderjit S. and Ghosh, Joydeep
Clustering with Bregman Divergences.
Journal of Machine Learning Research
2005
6
1705-1749
http://dblp.uni-trier.de/db/journals/jmlr/jmlr6.html#BanerjeeMDG05
bayesian, clustering, lda, mixture, models
Heinrich, G.
Parameter estimation for text analysis
Web: http://www. arbylon. net/publications/text-est. pdf
2005
http://scholar.google.de/scholar.bib?q=info:oe4R2fGvQaMJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0
bayesian, introduction, lda, tutorial
Hertzmann, Aaron
Introduction to Bayesian learning
SIGGRAPH '04: ACM SIGGRAPH 2004 Course Notes
ACM
2004
22
New York, NY, USA
Sophisticated computer graphics applications require complex models of appearance, motion, natural phenomena, and even artistic style. Such models are often difficult or impossible to design by hand. Recent research demonstrates that, instead, we can "learn" a dynamical and/or appearance model from captured data, and then synthesize realistic new data from the model. For example, we can capture the motions of a human actor and then generate new motions as they might be performed by that actor. Bayesian reasoning is a fundamental tool of machine learning and statistics, and it provides powerful tools for solving otherwise-difficult problems of learning about the world from data. Beginning from first principles, this course develops the general methodologies for designing learning algorithms and describes their application to several problems in graphics.
http://portal.acm.org/citation.cfm?id=1103900.1103922
http://doi.acm.org/10.1145/1103900.1103922
bayesian, introduction, learning
Murphy, Kevin
An introduction to graphical models
2001
Web
http://www.ai.mit.edu/~murphyk/Papers/intro_gm.pdf
bayesian, graphical, introduction, models
Cowell, R.
Introduction to inference for Bayesian networks
Learning in graphical models
1999
9–26
http://scholar.google.de/scholar.bib?q=info:BmltKf6AqYkJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0
bayesian, inference, networks
Jordan, Michael I. and Ghahramani, Zoubin and Jaakkola, Tommi S. and Saul, Lawrence K.
An Introduction to Variational Methods for Graphical Models
Mach. Learn.
Kluwer Academic Publishers
1999
37
183–233
2
Hingham, MA, USA
http://portal.acm.org/citation.cfm?id=339248.339252
http://dx.doi.org/10.1023/A:1007665907178
bayesian, graphical, models, variational
Cowell, R.
Advanced inference in Bayesian networks
Learning in Graphical Models. MIT Press
1998
http://scholar.google.de/scholar.bib?q=info:PZ3Aqxv-3FgJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0
bayesian, introduction, ml, networks
Jordan, M.
Learning in Graphical Models
MIT Press
1998
bayesian, graphical, learning, ml, model
Jordan, M.I.
Learning in graphical models
Kluwer Academic Publishers
1998
http://scholar.google.de/scholar.bib?q=info:EZqYGcIKUI8J:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0
bayesian, graphical, introduction, models
Buntine, Wray L.
Operations for Learning with Graphical Models
Journal of Artificial Intelligence Research
1994
2
159–225
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.696
bayesian, graphical, introduction, ml, models, notation, plate
AN, S.G.E.M. and AN, D.G.E.M.
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
IEEE Trans. Pattern Anal. Machine Intell
1984
6
721–741
http://scholar.google.de/scholar.bib?q=info:E_YCl1NmoesJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0
bayesian, distribution, estimation, gibbs, parameter, sampling