Journal ArticleDOI
Outlier detection for high-dimensional data
TLDR
An outlier detection procedure is proposed that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator and the cut-off value is obtained from the asymptotic distribution of the distance.Abstract:
Outlier detection is an integral component of statistical modelling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the asymptotic distribution of the distance, which enables us to control the Type I error and deliver robust outlier detection. Simulation studies show that the proposed method behaves well for high-dimensional data.read more
Citations
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Journal ArticleDOI
Progress in Outlier Detection Techniques: A Survey
TL;DR: This survey presents a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019 and categorizes them into different techniques from diverse outlier Detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods.
Proceedings ArticleDOI
Radar: residual analysis for anomaly detection in attributed networks
TL;DR: This paper investigates the problem of anomaly detection in attributed networks generally from a residual analysis perspective, and proposes a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection.
Journal ArticleDOI
Enhancing density-based clustering: Parameter reduction and outlier detection
TL;DR: The method, based on the concept of space stratification, efficiently identifies the different densities in the dataset and, accordingly, ranks the objects of the original space and performs a density based clustering taking into account the reverse-nearest-neighbor of the objects.
Journal ArticleDOI
Sliding Window-Based Fault Detection From High-Dimensional Data Streams
TL;DR: An angle-based subspace anomaly detection approach is proposed to detect low-dimensional subspace faults from high-dimensional datasets using the sliding window strategy and can be adaptive to the time-varying behavior of the monitored system.
Journal ArticleDOI
Discovering outlying aspects in large datasets
Nguyen Xuan Vinh,Jeffrey Chan,Simone Romano,James Bailey,Christopher Leckie,Kotagiri Ramamohanarao,Jian Pei +6 more
TL;DR: This paper investigates several open challenges faced by existing outlying aspects mining techniques and proposes novel solutions, including how to design effective scoring functions that are unbiased with respect to dimensionality and yet being computationally efficient, and how to efficiently search through the exponentially large search space of all possible subspaces.
References
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TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
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A fast algorithm for the minimum covariance determinant estimator
TL;DR: For small datasets, FAST-MCD typically finds the exact MCD, whereas for larger datasets it gives more accurate results than existing algorithms and is faster by orders.