Density estimation methods are often regarded as unsuitable for anomaly detection in high-dimensional data due to the difficulty of estimating multivariate probability distributions. Instead, the scores from popular distanceand localdensity-based methods, such as local outlier factor (LOF), are used as surrogates for probability densities. We question this infeasibility assumption and explore a...

The aim of detecting outliers in a multivariate sample can be pursued in di erent ways We investigate here the performance of several simultaneous multivariate outlier identi cation rules based on robust estimators of location and scale It has been shown that the use of estimators with high nite sample breakdown point in such procedures yields a good behaviour with respect to the prevention of ...

This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Densitybased Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object. Instead of...

Changes in the measured structural responses, such as deflections and modal properties, induced by damage could be significantly smaller than those by environmental effects such as temperature and temperature gradients. To make the structural health monitoring more reliable and applicable to real structures, it is highly desirable to develop a methodology to distinguish the changes due to the s...

Three emotional intelligence models are widely discussed in the literature: the ability model, trait model, and mixed model. This study introduces a model for understanding emotional intelligence: the synergy model. The emotional intelligence model presented in this study is simple and easy to understand. This study suggests that emotional intelligence is the synergy present in intelligence, wh...

Recent research in motion detection has shown that various outlier detection methods could be used for efficient detection of small moving targets. These algorithms detect moving objects as outliers in a properly defined attribute space, where outlier is defined as an object distinct from the objects in its neighborhood. In this paper, we compare the performance of two incremental outlier detec...