Outlier detection is an important topic in machine learning and has been used in a wide
range of applications. In this paper, we approach outlier detection as a binary-classification …

The term of Curse of Dimensionality implicitly expresses the challenge for anomaly detection
in a high-dimensional space. Because the distribution of anomalies in the high-dimensional …

In this work, we propose CBiGAN-a novel method for anomaly detection in images, where a
consistency constraint is introduced as a regularization term in both the encoder and …

It is a grand challenge to detect anomalies existing in subspaces from a high-dimensional
space. Most existing state-of-the-art methods implicitly or explicitly rely on distances. Since …

Data distribution presents sparsity in a high-dimensional space, thus difficulty affording
sufficient information to distinguish anomalies from normal instances. Moreover, a high …

Anomaly detection of high-dimensional data is a challenge because the sparsity of the data
distribution caused by high dimensionality hardly provides rich information distinguishing …

Outlier detection is an important task in data mining, and many technologies for it have been
explored in various applications. However, owing to the default assumption that outliers are …

In order to assure safety in self-driving cars, the Autonomous Drive functionality needs to
pass safety tests not only based on real scenarios collected from field driving tests, but also …

This paper provides a review and comparative analysis of trajectory outlier detection
methods. It presents the definition of outliers in trajectory data and the existing types to …

We propose unsupervised representation learning and feature extraction from dendrograms.
The commonly used Minimax distance measures correspond to building a dendrogram with …