IDENTIFICATION VIA COMPRESSED DATA

A new coding problem is introduced for a correlated source (X(n),Y-n)( n=1)(infinity). The observer of X(n) can transmit data depending on X( n) at a prescribed rate R, Based on these data the observer of Y-n tri es to identify whether for some distortion measure rho (like the Hammi ng distance) n(-1) rho(X(n),Y-n) less than or equal to d, a prescribed fidelity criterion. We investigate as functions of R and d the expone nts of two error probabilities, the probabilities for misacceptance, a nd the probabilities for misrejection, In the case where X(n) and Y-n are independent, we completely characterize the achievable region for the rate R and the exponents of two error probabilities; in the case w here X(n) and Y-n are correlated, we get some interesting partial resu lts for the achievable region, During the process, we develop a new me thod for proving converses, which is called ''The Inherently Typical S ubset Lemma,'' This new method goes considerably beyond the ''Entropy Characterization,'' the Image Size Characterization,'' and its extensi ons, It is conceivable that this new method has a strong impact on Mul tiuser Information Theory.