Ensemble equivalence and asymptotic equipartition property in information theory

There is a consensus in science that information theory and statistical physics have a close relationship but the literary proofs of the equivalence between most of the conceptions in the two disciplines are still missing. In this work, according to the statistical ensembles' description of the information sequences that are generated by the i.i.d. single variable and multivariate information source, the relationship between the ensemble equivalence and asymptotic equipartition property is established. We find that the description of information sequences in classical information theory is a special case of the canonical ensemble description of information sequences. They are both the maximum entropy approximation of the real signal generation. Vice versa, the conjugate microcanonical ensemble description of the information sequences under hard constraints satisfies the condition in real signal generation exactly. Thus, the microcanoincal ensemble description is closer to the real signal generation than the conjugate canonical ensemble, but the ensemble equivalence between the microcanonical and canonical ensemble in the thermodynamic limit guarantees the effectiveness of classical information theory, i.e., the asymptotic equipartition property in information theory is an isotope of ensemble equivalence from statistical physics

Local entropy and nonextensivity of networks ensembles

Nonextensivity is foreseeable in network ensembles, as heterogeneous interactions generally exist in complex networked systems that need to be described by network ensembles. But this nonextensivity has not been literatured proved yet. In this work, the existence of nonextensivity in the binary and weighted network ensembles is theoretically proved for the first time (both in the microcanonical and canonical ensemble) based on the finding of the local entropy's nonlinear change when new nodes are added to the network ensembles. This proof also shows that the existence of nonextensivity is the main difference between the network ensembles and other traditional models in statistical physics (Ising model)

Robust dynamic classifier selection for remote sensing image classification

Dynamic classifier selection (DCS) is a classification technique that, for each new sample to be classified, selects and uses the most competent classifier among a set of available ones. We here propose a novel DCS model (R-DCS) based on the robustness of its prediction: the extent to which the classifier can be altered without changing its prediction. In order to define and compute this robustness, we adopt methods from the theory of imprecise probabilities. Additionally, two selection strategies for R-DCS model are presented and are applied on remote sensing images. The experiment results demonstrate that our model successfully incorporates uncertainty with respect to the model parameters without losing the performance