Gaussian mixture learning via robust competitive agglomeration

Authors:
Zhiwu Lu;Yuxin Peng;Horace H. S. Ip
Affiliations:
Institute of Computer Science and Technology, Peking University, Beijing 100871, China and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong;Institute of Computer Science and Technology, Peking University, Beijing 100871, China;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
Venue:
Pattern Recognition Letters
Year:
2010

Citing 18
Cited 1

Structure learning in conditional probability models via an entropic prior and parameter extinction

Neural Computation
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research

IEEE Transactions on Pattern Analysis and Machine Intelligence
On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

Journal of Multivariate Analysis
Bayesian Analysis of Mixtures of Factor Analyzers

Neural Computation
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach

IEEE Transactions on Knowledge and Data Engineering
Fuzzy c-means clustering methods for symbolic interval data

Pattern Recognition Letters
Toward Objective Evaluation of Image Segmentation Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
A minimum description length objective function for groupwise non-rigid image registration

Image and Vision Computing
Segmentation of color images via reversible jump MCMC sampling

Image and Vision Computing
Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation

Pattern Recognition Letters
Clustering by competitive agglomeration

Pattern Recognition
Paper: Modeling by shortest data description

Automatica (Journal of IFAC)
Packet Video Error Concealment With Gaussian Mixture Models

IEEE Transactions on Image Processing
Statistical analysis of network traffic for adaptive faults detection

IEEE Transactions on Neural Networks
A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation

IEEE Transactions on Neural Networks

Constrained spectral clustering via exhaustive and efficient constraint propagation

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI

Quantified Score

Hi-index	0.10

Visualization

Abstract

When learning Gaussian mixtures from multivariate data, it is crucial to select the appropriate number of components and simultaneously avoid local optima. To resolve these problems, we follow the idea of competitive agglomeration which is originally used for fuzzy clustering and propose two robust algorithms for Gaussian mixture learning. Through some asymptotic analysis, we find that such robust competitive agglomeration can lead to automatic model selection on Gaussian mixtures and also make our algorithms less sensitive to initialization than the EM algorithm. Experiments demonstrate that our algorithms can achieve promising results just as our theoretic analysis.