Title :
Gaussian mixture density modeling, decomposition, and applications
Author :
Zhuang, Xinhua ; Huang, Yan ; Palaniappan, K. ; Zhao, Yunxin
Author_Institution :
Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA
fDate :
9/1/1996 12:00:00 AM
Abstract :
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
Keywords :
Gaussian processes; cellular biophysics; image classification; medical image processing; parameter estimation; statistical analysis; GMDD algorithm; Gaussian component; Gaussian mixture density decomposition; Gaussian mixture density modeling; automated cell classification; contaminated Gaussian density; decomposition; extraction; mixture distribution; noisy data; parameter estimation; probability densities; probability density estimation; recognition; recursive algorithm; robust statistical methods; signal characterization; waveform characterization; Character recognition; Clustering algorithms; Data mining; Gaussian distribution; Noise shaping; Parameter estimation; Recursive estimation; Robustness; Shape; Statistical analysis;
Journal_Title :
Image Processing, IEEE Transactions on