Outlier Detection Approach Survey for Imperfect Data Labels
The task of outlier detection is to identify data objects that are markedly different from or inconsistent with the normal set of data. However, in addition to normal data, there also exist limited negative examples or outliers in many applications, and data may be corrupted such that the outlier detection data is imperfectly labeled. These make outlier detection far more difficult than the traditional ones. Outlier detection approach to address data with imperfect labels and incorporate limited abnormal examples into learning. To deal with data with imperfect labels, use likelihood values for each input data which denote the degree of membership. Mainly approach works in two steps. In the first step, generate a pseudo training dataset by computing likelihood values of each example based on its local behavior. Kernel k-means clustering method and kernel LOF-based method to compute the likelihood values. In the second step, incorporate the generated likelihood values and limited abnormal examples into SVDDbased learning framework to build a more accurate classifier for global outlier detection. By integrating local and global outlier detection, the method explicitly handles data with imperfect labels and enhances the performance of outlier detection.