A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Method for Mining Regression Classes in Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust mixture modelling using the t distribution
Statistics and Computing
Image coding using transform vector quantization with training set synthesis
Signal Processing - Image and Video Coding beyond Standards
Unsupervised fuzzy clustering with multi-center clusters
Fuzzy Sets and Systems - Clustering and modeling
Feature-Space Analysis of Unstructured Meshes
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
A highly robust estimator for regression models
Pattern Recognition Letters
Sequential clustering by statistical methodology
Pattern Recognition Letters
Decomposition of mixed pixels based on bayesian self-organizing map and Gaussian mixture model
Pattern Recognition Letters
A fuzzy, nonparametric segmentation framework for DTI and MRI analysis
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Convolution on the n-sphere with application to PDF modeling
IEEE Transactions on Signal Processing
Fuzzy nonparametric DTI segmentation for robust cingulum-tract extraction
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
A genetic clustering algorithm using a message-based similarity measure
Expert Systems with Applications: An International Journal
A revision for gaussian mixture density decomposition algorithm
CIS'04 Proceedings of the First international conference on Computational and Information Science
A maximum profit coverage algorithm with application to small molecules cluster identification
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
A segmentation algorithm for jacquard images based on mumford-shah model
EGMM'04 Proceedings of the Seventh Eurographics conference on Multimedia
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We present a new approach to the modeling and decomposition of Gaussian mixtures by using robust statistical methods. The mixture distribution is viewed as a contaminated Gaussian density. Using this model and the model-fitting (MF) estimator, we propose a recursive algorithm called the Gaussian mixture density decomposition (GMDD) algorithm for successively identifying each Gaussian component in the mixture. The proposed decomposition scheme has advantages that are desirable but lacking in most existing techniques. In the GMDD algorithm the number of components does not need to be specified a priori, the proportion of noisy data in the mixture can be large, the parameter estimation of each component is virtually initial independent, and the variability in the shape and size of the component densities in the mixture is taken into account. Gaussian mixture density modeling and decomposition has been widely applied in a variety of disciplines that require signal or waveform characterization for classification and recognition. We apply the proposed GMDD algorithm to the identification and extraction of clusters, and the estimation of unknown probability densities. Probability density estimation by identifying a decomposition using the GMDD algorithm, that is, a superposition of normal distributions, is successfully applied to automated cell classification. Computer experiments using both real data and simulated data demonstrate the validity and power of the GMDD algorithm for various models and different noise assumptions