IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted Parzen Windows for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multivariate Density Estimation: an SVM Approach
Multivariate Density Estimation: an SVM Approach
EBEM: An Entropy-based EM Algorithm for Gaussian Mixture Models
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate maximum likelihood hyperparameter estimation for Gibbs priors
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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In this paper, we propose an attraction-repulsion expectation-maximization (AREM) algorithm for image reconstruction and sensor field estimation. We rely on a new method for density estimation to address the problems of image reconstruction from limited samples and sensor field estimation from randomly scattered sensors. Density estimation methods often suffer from undesirable phenomena such as over-fitting and over-smoothing. Specifically, various density estimation techniques based on a Gaussian mixture model (GMM) tend to cluster the Gaussian functions together, thus resulting in over-fitting. On the other hand, other approaches repel the Gaussian functions and yield over-smooth density estimates. We propose a method that seeks an equilibrium between over-fitting and oversmoothing in density estimation by incorporating attraction and repulsion forces among the Gaussian functions and determining the optimal balance between the competing forces experimentally. We model the attractive and repulsive forces by introducing the Gibbs and inverse Gibbs distributions, respectively. The maximization of the likelihood function augmented by the Gibbs density mixture is solved under the expectation-maximization (EM) method. Computer simulation results are provided to demonstrate the effectiveness of the proposed AREM algorithm in image reconstruction and sensor field estimation.