Maximum Likelihood Outlier Detection

• Maximum Likelihood Outlier Detection (MLOD) is an inlier-based outlier detection algorithm. The problem of inlier-based outlier detection is to find outliers in a set of samples (called the evaluation set) using another set of samples which consists only of inliers (called the model set). MLOD orders the samples in the evaluation set according to their degree of outlyingness. The degree of outlyingness is measured by the ratio of probability densities of evaluation and model samples. The ratio is estimated by the density-ratio estimation method KLIEP.