Extreme value statistics for novelty detection in biomedical data processing

2000 ◽  
Vol 147 (6) ◽  
pp. 363-367 ◽  
Author(s):  
S.J. Roberts
Keyword(s):  
Data Processing ◽  
Novelty Detection ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Biomedical Data

Bayesian Extreme Value Statistics for Novelty Detection in Gas-Turbine Engines

2008 ◽  
Author(s):  
David A. Clifton ◽  
Lionel Tarassenko ◽  
Nicholas McGrogan ◽  
Dennis King ◽  
Steve King ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Novelty Detection ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Turbine Engines ◽  
Gas Turbine Engines

Novelty Detection with Multivariate Extreme Value Statistics

2010 ◽  
Vol 65 (3) ◽  
pp. 371-389 ◽  
Author(s):  
David Andrew Clifton ◽  
Samuel Hugueny ◽  
Lionel Tarassenko
Keyword(s):  
Novelty Detection ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Multivariate Extreme Value

Structural Damage Classification Using Extreme Value Statistics

2005 ◽  
Vol 127 (1) ◽  
pp. 125-132 ◽  
Author(s):  
Hoon Sohn ◽  
David W. Allen ◽  
Keith Worden ◽  
Charles R. Farrar
Keyword(s):  
Extreme Values ◽  
Novelty Detection ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Series Data ◽  
Identification Algorithm ◽  
Damage Classification ◽  
Mass System ◽  
Damage Diagnosis ◽  
Normality Assumption

The first and most important objective of any damage identification algorithm is to ascertain with confidence if damage is present or not. Many methods have been proposed for damage detection based on ideas of novelty detection founded in pattern recognition and multivariate statistics. The philosophy of novelty detection is simple. Features are first extracted from a baseline system to be monitored, and subsequent data are then compared to see if the new features are outliers, which significantly depart from the rest of population. In damage diagnosis problems, the assumption is that outliers are generated from a damaged condition of the monitored system. This damage classification necessitates the establishment of a decision boundary. Choosing this threshold value is often based on the assumption that the parent distribution of data is Gaussian in nature. While the problem of novelty detection focuses attention on the outlier or extreme values of the data, i.e., those points in the tails of the distribution, the threshold selection using the normality assumption weights the central population of data. Therefore, this normality assumption might impose potentially misleading behavior on damage classification, and is likely to lead the damage diagnosis astray. In this paper, extreme value statistics is integrated with the novelty detection to specifically model the tails of the distribution of interest. Finally, the proposed technique is demonstrated on simulated numerical data and time series data measured from an eight degree-of-freedom spring-mass system.

Correct problem statements in biomedical data processing

1976 ◽  
Author(s):  
N. I. Moiseeva ◽  
M. Yu. Simonov ◽  
V. M. Sysuev
Keyword(s):  
Data Processing ◽  
Biomedical Data ◽  
Correct Problem

On the spectral shape of SEP events: An extreme value statistics approach

2013 ◽  
Author(s):  
M. Laurenza ◽  
G. Consolini ◽  
M. Storini ◽  
A. Damiani
Keyword(s):  
Extreme Value ◽  
Spectral Shape ◽  
Extreme Value Statistics
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