Algorithms for optimal outlier removal

Improved Hybrid Clustering and Distance-based Technique for Outlier Removal

Outliers detection is a task that finds objects that are dissimilar or inconsistent with respect to the remaining data. It has many uses in applications like fraud detection, network intrusion detection and clinical diagnosis of diseases. Using clustering algorithms for outlier detection is a technique that is frequently used. The clustering algorithms consider outlier detection only to the poi...

A Comparative Study on Outlier Removal from a Large-scale Dataset using Unsupervised Anomaly Detection

Outlier removal from training data is a classical problem in pattern recognition. Nowadays, this problem becomes more important for large-scale datasets by the following two reasons: First, we will have a higher risk of “unexpected” outliers, such as mislabeled training data. Second, a large-scale dataset makes it more difficult to grasp the distribution of outliers. On the other hand, many uns...

Optimal Outlier Removal in High-Dimensional Spa es

Outlier Removal for Motion Tracking by Subspace Separation

Many feature tracking algorithms have been proposed for motion segmentation, but the resulting trajectories are not necessarily correct. In this paper, we propose a technique for removing outliers based on the knowledge that correct trajectories are constrained to be in a subspace of their domain. We first fit an appropriate subspace to the detected trajectories using RANSAC and then remove out...

Guaranteed Outlier Removal for Point Cloud Registration with Correspondences

An established approach for 3D point cloud registration is to estimate the registration function from 3D keypoint correspondences. Typically, a robust technique is required to conduct the estimation, since there are false correspondences or outliers. Current 3D keypoint techniques are much less accurate than their 2D counterparts, thus they tend to produce extremely high outlier rates. A large ...