Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Hidden Markov Random Field Model Selection Criteria Based on Mean Field-Like Approximations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy
IEEE Transactions on Information Theory
Properties of cross-entropy minimization
IEEE Transactions on Information Theory
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We present an entropic component analysis for identifying key parameters or variables and the joint effects of various parameters that characterize complex systems. This approach identifies key parameters through solving the variable selection problem. It consists of two steps. First, a Bayesian approach is utilized to convert the variable selection problem into the model selection problem. Second, the model selection is achieved uniquely by evaluating the information difference of models by relative entropies of these models and a reference model. We study a geological sample classification problem, where a brine sample from Texas and Oklahoma oil field is considered, to illustrate and examine the proposed approach. The results are consistent with qualitative analysis of the lithology and quantitative discriminant function analysis. Furthermore, the proposed approach reveals the joint effects of the parameters, while it is unclear from the discriminant function analysis. The proposed approach could be thus promising to various geological data analysis.