Infomax
Infomax', or the principle of maximum information preservation, is an optimization principle for artificial neural networks and other information processing systems. It prescribes that a function that maps a set of input values x {\displaystyle x} to a set of output values z ( x ) {\displaystyle z(x)} should be chosen or learned so as to maximize the average Shannon mutual information between x {\displaystyle x} and z ( x ) {\displaystyle z(x)} , subject to a set of specified constraints and/or noise processes.