Anomaly detection is a classical but worthwhile problem, and many deep learning-based
anomaly detection algorithms have been proposed, which can usually achieve better …

Anomaly detection is a hot and practical problem. Most of the existing research is based on
the model of the generative model, which judges abnormalities by comparing the data errors …

Bearing failure in freight trains can directly affect operational safety. Therefore, bearing
condition evaluation is of practical and critical significance. In this paper, a new method of …

An outlier is an observation or a data point that is far from rest of the data points in a given
dataset or we can be said that an outlier is away from the center of mass of observations …

It is well-known that the existing theoretical models for outlier detection make assumptions
that may not reflect the true nature of outliers in every real application. With that in mind, this …

Multiscale geometric feature extraction for high-dimensional and non-Euclidean data with
applications Page 1 The Annals of Statistics 2021, Vol. 49, No. 2, 988–1010 https://doi.org/10.1214/20-AOS1988 …

We propose an extension of the Angle-Based Outlier Detection (ABOD) technique by
combining it with a Dictionary Learning (DL) problem. The ABOD method benefits from this …

In this paper, we consider the problem of non-linear dimensionality reduction under
uncertainty, both from a theoretical and algorithmic perspectives. Since real-world data …

In many cities, there is often a disconnect between the government and the people it serves.
Many cities have systems in place where citizens can submit ticket requests to help improve …