Finite mixture models with negative components

Authors:
Baibo Zhang;Changshui Zhang
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, P.R. China;State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, P.R. China
Venue:
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2005

Citing 3
Cited 0

Algorithms for clustering data

Algorithms for clustering data
Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the manifolds of images of handwritten digits

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mixture models, especially mixtures of Gaussian, have been widely used due to their great flexibility and power. Non-Gaussian clusters can be approximated by several Gaussian components, however, it can not always acquire appropriate results. By cancelling the nonnegative constraint to mixture coefficients and introducing a new concept of “negative components”, we extend the traditional mixture models and enhance their performance without increasing the complexity obviously. Moreover, we propose a parameter estimation algorithm based on an iteration mechanism, which can effectively discover patterns of “negative components”. Experiments on some synthetic data testified the reasonableness of the proposed novel model and the effectiveness of the parameter estimation algorithm.