Cross identification: Method and application

Pattern identification algorithms can be exploited for single-class or multi-class identification problems. In conventional applications, identification algorithms are based on functions of measured features. However in some cases, we already have an identification algorithm but the measured features are different from the features expected by the algorithm. In such cases, we can first estimate the needed features from the measured features and then use the identification algorithm with the estimated features. This paper proposes a framework for such Cross Identification problems together with an application to a genetic identification problem.