Handbook of pattern recognition & computer vision
Bayesian Ying-Yang machine, clustering and number of clusters
Pattern Recognition Letters - special issue on pattern recognition in practice V
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
On bootstrapping the number of components in finite mixtures of Poisson distributions
Statistics and Computing
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures
Neural Computation
Neural Processing Letters
A fast fixed-point BYY harmony learning algorithm on Gaussian mixture with automated model selection
Pattern Recognition Letters
Two-way Poisson mixture models for simultaneous document classification and word clustering
Computational Statistics & Data Analysis
On the correct convergence of the EM algorithm for Gaussian mixtures
Pattern Recognition
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Texture classification using spectral histograms
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Survey A survey on applications of the harmony search algorithm
Engineering Applications of Artificial Intelligence
Kernel k'-means algorithm for clustering analysis
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Finite mixture is widely used in the fields of information processing and data analysis. However, its model selection, i.e., the selection of components in the mixture for a given sample data set, has been still a rather difficult task. Recently, the Bayesian Ying-Yang (BYY) harmony learning has provided a new approach to the Gaussian mixture modeling with a favorite feature that model selection can be made automatically during parameter learning. In this paper, based on the same BYY harmony learning framework for finite mixture, we propose an adaptive gradient BYY learning algorithm for Poisson mixture with automated model selection. It is demonstrated well by the simulation experiments that this adaptive gradient BYY learning algorithm can automatically determine the number of actual Poisson components for a sample data set, with a good estimation of the parameters in the original or true mixture where the components are separated in a certain degree. Moreover, the adaptive gradient BYY learning algorithm is successfully applied to texture classification.