Modern compilers explore various large and complex transformation spaces in an iterative manner in search for high performance for a given program. Machine learning techniques have recently been used by compilers to capture the features of a given program and find out useful heuristics from their prior experience with similar programs. However, the success of such learningbased approaches relies heavily on the experience that a compiler has obtained optimization, which is limited in practice. This hinders its applicability in general scenarios. To tackle this pitfall, we use a reverse Knearest neighbor (RKNN) algorithm to help a compiler to decide whether to use the existing prior experience directly, or turn to launch an optimization space search for outlier programs instead. Preliminary experimental results are given to demonstrate its effectiveness.