In biomedical analytics one of the major criteria for the characterization of similarities between measured data items is correlation. We demonstrate the use of the formal derivative of Pearson correlation for gradientbased optimization of data models. Firstly, individual data attributes can be rated according to their impact on pairwise data relationships, analogous to the variance measure in Euclidean space. Secondly, a versatile method is presented to perform faithful multi-dimensional

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... g from a high-dimensional space of source data to a low-dimensional target space, a method driven by maximization of correlation between distances of static source data and adaptive target vectors. As shown for mass spectroscopy data, a combination of attribute rating and data visualization helps revealing interesting data properties.