Intrinsic dimension estimation based on maximum likelihood estimator and reverse k nearest neighbors

We propose an algorithm for estimating the intrinsic dimension of a data set derived by applying the maximum likelihood estimator and analyzing both k nearest neighbor and reverse k nearest neighbors. Which can overcome the limitations of shortcut problem and bias caused by abnormal sampling density distribution when using only k nearest neighbor. It produces good results on some simulated and real datasets.