Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
Fuzzy clustering using scatter matrices
Computational Statistics & Data Analysis - Special issue on classification
ML estimation of the multivariate t distribution and the EM algorithm
Journal of Multivariate Analysis
A procedure for the detection of multivariate outliers
Computational Statistics & Data Analysis
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Gaussian mixture density modeling, decomposition, and applications
IEEE Transactions on Image Processing
A mixture model approach for the analysis of microarray gene expression data
Computational Statistics & Data Analysis
Alive Fishes Species Characterization from Video Sequences
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Bayesian analysis of mixture modelling using the multivariate t distribution
Statistics and Computing
Robust mixture modelling using multivariate t-distribution with missing information
Pattern Recognition Letters
Journal of Multivariate Analysis
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
WBE'06 Proceedings of the 5th IASTED international conference on Web-based education
On fast supervised learning for normal mixture models with missing information
Pattern Recognition
Stylized facts of financial time series and hidden semi-Markov models
Computational Statistics & Data Analysis
Neural Networks
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
Robust mixture modeling using the skew t distribution
Statistics and Computing
Robust mixtures in the presence of measurement errors
Proceedings of the 24th international conference on Machine learning
A content-based image retrieval scheme allowing for robust automatic personalization
Proceedings of the 6th ACM international conference on Image and video retrieval
Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods
Journal of Multivariate Analysis
Robust fuzzy clustering using mixtures of Student's-t distributions
Pattern Recognition Letters
Robust Visual Mining of Data with Error Information
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Exploratory Characterization of Outliers in a Multi-centre 1H-MRS Brain Tumour Dataset
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Adaptive importance sampling in general mixture classes
Statistics and Computing
Model-based clustering with non-elliptically contoured distributions
Statistics and Computing
Robust probabilistic PCA with missing data and contribution analysis for outlier detection
Computational Statistics & Data Analysis
The mixtures of Student's t-distributions as a robust framework for rigid registration
Image and Vision Computing
Factor analysis latent subspace modeling and robust fuzzy clustering using t-distributions
IEEE Transactions on Fuzzy Systems
Multivariate Student-t self-organizing maps
Neural Networks
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Constrained monotone EM algorithms for mixtures of multivariate t distributions
Statistics and Computing
Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation
Journal of Mathematical Imaging and Vision
Robust analysis of MRS brain tumour data using t-GTM
Neurocomputing
Mixture of the robust L1 distributions and its applications
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Improving the robustness to outliers of mixtures of probabilistic PCAs
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Robust mixture modeling using multivariate skew t distributions
Statistics and Computing
Robust mixture modeling based on scale mixtures of skew-normal distributions
Computational Statistics & Data Analysis
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
Robust mixture clustering using Pearson type VII distribution
Pattern Recognition Letters
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Robust curve clustering based on a multivariate t-distribution model
IEEE Transactions on Neural Networks
Modeling Hong Kong's stock index with the Student t-mixture autoregressive model
Mathematics and Computers in Simulation
The infinite Student's t-mixture for robust modeling
Signal Processing
Robust clustering algorithms based on finite mixtures of multivariate t distribution
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Robust speaker identification based on t-distribution mixture model
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
A robust EM clustering algorithm for Gaussian mixture models
Pattern Recognition
Multivariate mixture modeling using skew-normal independent distributions
Computational Statistics & Data Analysis
Robust Bayesian Clustering for Replicated Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
International Journal of Data Analysis Techniques and Strategies
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation
Expert Systems with Applications: An International Journal
Cartogram visualization for nonlinear manifold learning models
Data Mining and Knowledge Discovery
A finite mixture model for detail-preserving image segmentation
Signal Processing
A robust hidden semi-Markov model with application to aCGH data processing
International Journal of Data Mining and Bioinformatics
Dimension reduction for model-based clustering via mixtures of multivariate $$t$$t-distributions
Advances in Data Analysis and Classification
Using conditional independence for parsimonious model-based Gaussian clustering
Statistics and Computing
Robust mixture regression using the t-distribution
Computational Statistics & Data Analysis
A multivariate linear regression analysis using finite mixtures of t distributions
Computational Statistics & Data Analysis
Model-based clustering via linear cluster-weighted models
Computational Statistics & Data Analysis
Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition
Computational Statistics & Data Analysis
Parsimonious skew mixture models for model-based clustering and classification
Computational Statistics & Data Analysis
Pair-copula based mixture models and their application in clustering
Pattern Recognition
Finite mixtures of multivariate skew t-distributions: some recent and new results
Statistics and Computing
A comparative study of novel robust clustering algorithms
Intelligent Data Analysis
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Normal mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster sets of continuous multivariate data. However, for a set of data containing a group or groups of observations with longer than normal tails or atypical observations, the use of normal components may unduly affect the fit of the mixture model. In this paper, we consider a more robust approach by modelling the data by a mixture of t distributions. The use of the ECM algorithm to fit this t mixture model is described and examples of its use are given in the context of clustering multivariate data in the presence of atypical observations in the form of background noise.