Abstract: A data compression algorithm which computes from a given probability density, in continuous or histogram form, a “probability centrifuge” probability density and also the Fisher information of the density. The density preserves Shannon entropy of the original density and the Fisher information represents the minimum Fisher information obtainable by “lateral” adiabatic reduction. The data compression algorithm can alternately be used to perform a “vertically” adiabatic reduction, a “radial” adiabatic reduction, or a “sectoral” adiabatic reduction. Said algorithm may provide alternate information for applications such as image reconstruction and protein folding analysis.