Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

We propose a one-class neural network (OC-NN) model to detect anomalies in complex
data sets. OC-NN combines the ability of deep networks to extract a progressively rich …

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 …

Background modeling for foreground detection is often used in different applications to
model the background and then detect the moving objects in the scene like in video …

Anomaly detection is playing an increasingly important role in hyperspectral image (HSI)
processing. The traditional anomaly detection methods mainly extract knowledge from the …

Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

Deep generative models have demonstrated their effectiveness in learning latent
representation and modeling complex dependencies of time series. In this article, we …

PCA is a classical statistical technique whose simplicity and maturity has seen it find
widespread use for anomaly detection. However, it is limited in this regard by being sensitive …

Data clustering, which is to partition the given data into different groups, has attracted much
attention. Recently various effective algorithms have been developed to tackle the task …