Anomaly Detection in High-Dimensional Data

Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences

This paper addresses the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. 1 The approach taken uses unsupervised clustering of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by detailed analysis of outliers to detect anomalies. As the LCS measure is expensive to compute, the first part of the pap...

Methods for regression analysis in high-dimensional data

By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...

Mitigating the Insider Threat with High-dimensional Anomaly Detection

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and eac...

Anomaly Detection in Gps Data

Modern machine learning techniques provide robust approaches for datadriven modeling and critical information extraction, while human experts hold the advantage of possessing high-level intelligence and domain-specific expertise. We combine the power of the two for anomaly detection in GPS data by integrating them through a visualization and human-computer interaction interface. In this thesis ...