Outliers

Visualizing Outliers

Outliers have more than two centuries’ history in the field of statistics. Recently, they have become a focal topic because of their relevance to terrorism, network intrusions, financial fraud, and other areas where rare events are critical to understanding a process. This paper presents a new algorithm, called hdoutliers, for detecting multidimensional outliers. It is unique for a) dealing wit...

Memorable outliers

cannot be inhibited (hence its attraction as a memory maintenance molecule); however, pharmacological inhibitors of PKMz are available. Many studies have now shown that inhibition of PKMz appears to erase consolidated memories, as well as established LTP [18]. But not all forms of consolidated memories are susceptible to disruption by inhibition of PKMz [19]. Furthermore, the specificity of the...

Outliers and Bayesian Inference

In this paper we report about an investigation in which we studied the properties of Bayes’ inferred neural network classifiers in the context of outlier detection. The problem of misclassification due to outliers in the test data is seen as a serious problem in safety critical environments. We compare the usual way to deal with uncertainty in the Bayesian framework with a new approach based on...

Identifying Multi-instance Outliers

This paper studies a new data mining problem called multiinstance outlier identification. This problem arises in tasks where each sample consists of many alternative feature vectors (instances) that describe it. This paper defines the multi-instance outliers and analyzes the basic types of multiinstance outliers. Two general identification approaches are proposed based on the state-of-the-art (...

Eliminating Outliers in Motion