An outlier is a datum that is far from other data points in which it occurs. It can have a
considerable impact on the output. Therefore, removing or resolving it before the analysis is …

Outlier detection technologies play an important role in various application domains. Most
existing outlier detection algorithms have difficulty detecting outliers that are mixed within …

Road anomaly detection aims to find a small group of roads that are exceptional with respect
to the rest of the physical links in a transportation network, posing great challenges for …

Self-supervised visual representation learning (SSL) aims to extract the most distinctive
features from unlabeled datasets to overcome challenges of labor-intensive and time …

Outlier detection is the discovery of some objects that are significantly different from many
objects in data, and it is widely used in important fields. Most existing methods are based on …

The adoption of electronic health records (EHRs) has created opportunities to analyse
historical data for predicting clinical outcomes and improving patient care. However, non …

This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive
Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is …

Advancements in data availability and computational techniques, including machine
learning, have transformed the field of bioinformatics, enabling the robust analysis of …

We revisit score combinations for outlier detection ensembles (ODEs). Different detectors'
scores vary in ranges and scales. Normalization transforms these into a common range. We …

This article is focused on the automatic detection of the corrupted or inappropriate
responses in questionnaire data using unsupervised outliers detection. The questionnaire …