Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

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 …

Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

The objective of change-point detection is to discover abrupt property changes lying behind
time-series data. In this paper, we present a novel statistical change-point detection …

Theory, algorithms, and applications of machine learning techniques to overcome" covariate
shift" non-stationarity. As the power of computing has grown over the past few decades, the …

This paper proposes two robust statistical techniques for outlier detection and robust
saliency features, such as surface normal and curvature, estimation in laser scanning 3D …

Ensemble analysis is a popular method used to improve the accuracy of various data mining
algorithms. Ensemble methods combine the outputs of multiple algorithms or base detectors …

We consider the problem of learning a binary classifier only from positive data and
unlabeled data (PU learning). Recent methods of PU learning commonly assume that the …