Elements of information theory
Elements of information theory
An estimator of the mutual information based on a criterion for independence
Computational Statistics & Data Analysis
An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing
Dependence, correlation and Gaussianity in independent component analysis
The Journal of Machine Learning Research
Neural Computation
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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SPECT images using radiopharmaceuticals are limited by noise caused by both random and systematic uncertainties. All the efforts so far have been directed only to minimize the random uncertainty and no attempt has ever been made to minimize the noise due to systematic uncertainty. As these radiopharmaceuticals encounter many systematic uncertainties during their formation, we constructed the covariance matrix with some of these systematic uncertainties for the gamma count rate of ^1^1^3^mIn. We describe the algorithm we have developed based on the technique of determinant inequalities and the concept of minimization of mutual information to process the covariance matrix element by element to minimize the noise caused by systematic uncertainty in the SPECT imaging of ^1^1^3^mIn and its utility to experimentalists to design and improve their process of measurement and instrumentation.