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187
Robust Bayesian Max-Margin Clustering
"... We present max-margin Bayesian clustering (BMC), a general and robust frame-work that incorporates the max-margin criterion into Bayesian clustering models, as well as two concrete models of BMC to demonstrate its flexibility and effective-ness in dealing with different clustering tasks. The Dirichl ..."
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Cited by 1 (1 self)
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We present max-margin Bayesian clustering (BMC), a general and robust frame-work that incorporates the max-margin criterion into Bayesian clustering models, as well as two concrete models of BMC to demonstrate its flexibility and effective-ness in dealing with different clustering tasks
Max-Margin Offline Pedestrian Tracking with Multiple Cues
"... In this paper, we introduce MMTrack, a hybrid single pedestrian tracking algorithm that puts together the advantages of descriptive and discriminative approaches for tracking. Specifically, we combine the idea of cluster-based appearance modeling and online tracking and employ a max-margin criterion ..."
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Cited by 2 (2 self)
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In this paper, we introduce MMTrack, a hybrid single pedestrian tracking algorithm that puts together the advantages of descriptive and discriminative approaches for tracking. Specifically, we combine the idea of cluster-based appearance modeling and online tracking and employ a max-margin
Max-margin Clustering: Detecting Margins from Projections of Points on Lines
"... Given a unlabelled set of points X ∈ R N belonging to k groups, we propose a method to identify cluster assignments that provides maximum separating margin among the clusters. We address this problem by exploiting sparsity in data points inherent to margin regions, which a max-margin classifier woul ..."
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Cited by 2 (0 self)
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Given a unlabelled set of points X ∈ R N belonging to k groups, we propose a method to identify cluster assignments that provides maximum separating margin among the clusters. We address this problem by exploiting sparsity in data points inherent to margin regions, which a max-margin classifier
Robust Bayesian clustering
- Neural Networks
, 2007
"... A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algorithm leads to (i) robust density estimation, (ii) robust clustering and (iii) robust automatic model selection. Gaussian mixture models are learning machines which are based on a divide-and-conquer ap ..."
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Cited by 14 (1 self)
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A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algorithm leads to (i) robust density estimation, (ii) robust clustering and (iii) robust automatic model selection. Gaussian mixture models are learning machines which are based on a divide
Robust bayesian mixture modelling
- Neurocomputing
, 2005
"... Abstract. Bayesian approaches to density estimation and clustering using mixture distributions allow the automatic determination of the number of components in the mixture. Previous treatments have focussed on mixtures having Gaussian components, but these are well known to be sensitive to outliers. ..."
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Cited by 35 (0 self)
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Abstract. Bayesian approaches to density estimation and clustering using mixture distributions allow the automatic determination of the number of components in the mixture. Previous treatments have focussed on mixtures having Gaussian components, but these are well known to be sensitive to outliers
Bayesian cluster ensembles
- In Proceedings of the 9th SIAM International Conference on Data Mining
, 2009
"... Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of the basic cluster ensemble problem, notably including cluster ensembles with missing values, as well as row-distributed or ..."
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Cited by 24 (2 self)
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Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of the basic cluster ensemble problem, notably including cluster ensembles with missing values, as well as row
Bayesian Ying-Yang machine, clustering and number of clusters
- Pattern Recognition Letters
, 1997
"... It is shown that a particular case of the Bayesian Ying--Yang learning system and theory reduces to the maximum likelihood learning of a finite mixture, from which we have obtained not only the EM algorithm for its parameter estimation Z and its various approximate but fast algorithms for clustering ..."
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Cited by 31 (11 self)
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to be more robust in learning. Finally, experimental results are provided. q 1997 Elsevier Science B.V. Keywords: Bayesian Ying--Yang machine; Number of clusters; Finite mixture; Cluster analysis 1.
Bayesian Methods for Graph Clustering
, 2008
"... Editor: Networks are used in many scientific fields such as biology, social science, and information technology. They aim at modelling, with edges, the way objects of interest, represented by vertices, are related to each other. Looking for clusters of vertices, also called communities or modules, h ..."
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Cited by 5 (3 self)
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of classes in the latent structure. In this paper, we show how the Block-Clustering model can be described in a full Bayesian framework and how the posterior distribution, of all the parameters and latent variables, can be approximated efficiently applying Variational Bayes (VB). We also propose a new non
Robust speaker clustering in eigenspace
- Inst. for Human-Machine-Communication, Technische Universitat Munchen, Munich,Germany,2002 IEEE
"... In this paper we propose a speaker clustering scheme working in ’Eigenspace’. Speaker models are transformed to a low-dimensional subspace using ’Eigenvoices’. For the speaker clustering procedure simple distance measures, e.g. Euklidean distance can be applied. Moreover, clustering can be accomplis ..."
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Cited by 10 (0 self)
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In this paper we propose a speaker clustering scheme working in ’Eigenspace’. Speaker models are transformed to a low-dimensional subspace using ’Eigenvoices’. For the speaker clustering procedure simple distance measures, e.g. Euklidean distance can be applied. Moreover, clustering can
Nonparametric Bayesian Co-clustering Ensembles
"... A nonparametric Bayesian approach to co-clustering ensembles is presented. Similar to clustering ensembles, coclustering ensembles combine various base co-clustering results to obtain a more robust consensus co-clustering. To avoid pre-specifying the number of co-clusters, we specify independent Dir ..."
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Cited by 12 (1 self)
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A nonparametric Bayesian approach to co-clustering ensembles is presented. Similar to clustering ensembles, coclustering ensembles combine various base co-clustering results to obtain a more robust consensus co-clustering. To avoid pre-specifying the number of co-clusters, we specify independent
Results 1 - 10
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187