Results 1 - 10 of 187

Robust Bayesian Max-Margin Clustering

by Changyou Chen, Jun Zhu, Xinhua Zhang
"... 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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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

by Bahman Yari, Saeed Khanloo, Mani Ranjbar, Ze-nian Li, Nicolas Saunier, Tarek Sayed, Greg Mori
"... 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 ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
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

by Raghuraman Gopalan, Jagan Sankaranarayanan
"... 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 ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
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

by Cédric Archambeau, Michel Verleysen - 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 ..."
Abstract - Cited by 14 (1 self) - Add to MetaCart
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

by Christopher M. Bishop, Markus Svensén - 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. ..."
Abstract - Cited by 35 (0 self) - Add to MetaCart
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

by Hongjun Wang, Hanhuai Shan, Arindam Banerjee - 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 ..."
Abstract - Cited by 24 (2 self) - Add to MetaCart
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

by Lei Xu - 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 ..."
Abstract - Cited by 31 (11 self) - Add to MetaCart
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

by Pierre Latouche, Etienne Birmelé, Christophe Ambroise, P. Latouche, E. Birmelé, C. Ambroise, Pierre Latouche, Etienne Birmelé, Christophe Ambroise , 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 ..."
Abstract - Cited by 5 (3 self) - Add to MetaCart
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

by R. Faltlhauser, G. Ruske - 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 ..."
Abstract - Cited by 10 (0 self) - Add to MetaCart
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

by Pu Wang, Kathryn B. Laskey, Carlotta Domeniconi, Michael I. Jordan
"... 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 ..."
Abstract - Cited by 12 (1 self) - Add to MetaCart
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 of 187