Comparative analysis of statistical pattern recognition methods in high dimensional settings

1994 ◽  
Vol 27 (8) ◽  
pp. 1065-1077 ◽  
Author(s):  
Stefan Aeberhard ◽  
Danny Coomans ◽  
Olivier de Vel
Keyword(s):  
Pattern Recognition ◽  
Comparative Analysis ◽  
Statistical Pattern Recognition ◽  
High Dimensional ◽  
Pattern Recognition Methods ◽  
Statistical Pattern

scholarly journals The factors affecting competitiveness of companies: contribution and limits of the statistical pattern recognition methods

2011 ◽  
Vol 59 (7) ◽  
pp. 69-80
Author(s):  
Ladislav Blažek ◽  
Pavel Pudil ◽  
Jiří Špalek
Keyword(s):  
Pattern Recognition ◽  
Statistical Pattern Recognition ◽  
Economic Success ◽  
Holding Companies ◽  
Factors Affecting ◽  
Pattern Recognition Methods ◽  
Statistical Pattern ◽  
Potential Factors ◽  
Limited Partnerships ◽  
Typical Configuration

The paper elaborates the methodical side of empirical research of factors influencing the economic success of companies. The analysis is based on the selective sample of more than 400 stock listed (share holding) companies and limited partnerships located in the Czech Republic. The main goal of the research is to verify, methodically and theoretically, the hypothesis that there is significant mutual dependency between certain types of economic success of companies and a certain typical configuration of values of selected characteristics which describe these companies. The paper concentrates on an analysis of applying the statistical pattern recognition methodology in the course of verifying this hypothesis. Our analysis confirms the potential gains connected with the method. Within the sample we identified group of potential factors of competitiveness which can characterize the interdependence between competitiveness and economic performance.

The application of statistical pattern recognition methods for damage detection to field data

2008 ◽  
Vol 17 (6) ◽  
pp. 065023 ◽  
Author(s):  
A Cheung ◽  
C Cabrera ◽  
P Sarabandi ◽  
K K Nair ◽  
A Kiremidjian ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Field Data ◽  
Statistical Pattern Recognition ◽  
Pattern Recognition Methods ◽  
Statistical Pattern

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

2018 ◽  
Vol 18 (5-6) ◽  
pp. 1416-1443 ◽  
Author(s):  
Alireza Entezami ◽  
Hashem Shariatmadar ◽  
Abbas Karamodin
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Statistical Pattern Recognition ◽  
Data Driven ◽  
High Dimensional ◽  
Autoregressive Modeling ◽  
Damage Diagnosis ◽  
Leibler Divergence ◽  
Statistical Pattern

Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article is to propose an innovative residual-based feature extraction approach based on AutoRegressive modeling and a novel statistical distance method named as Partition-based Kullback–Leibler Divergence for damage detection and localization by using randomly high-dimensional damage-sensitive features under environmental and operational variability. The key novel element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms for extracting the residuals of AutoRegressive model as the main damage-sensitive features. This technique brings the great benefit of reducing the computational time and storage space for feature extraction in long-term monitoring conditions. The major contribution of Partition-based Kullback–Leibler Divergence method is to exploit a partitioning strategy for dividing random features into individual partitions and utilize numerical information of partitioning in distance calculation rather than directly applying random samples. Dealing with the major challenging issue of using the high-dimensional features in decision making and applicability to both correlated and uncorrelated random datasets are the main advantages of Partition-based Kullback–Leibler Divergence method. The accuracy and reliability of the proposed approaches are experimentally validated by two well-known benchmark structures. The stationarity and linearity of measured vibration responses for using in AutoRegressive modeling are evaluated by two hypothesis tests. Comparative studies are also conducted to demonstrate the superiority of the proposed methods over some exciting state-of-the-art techniques. Results show that the methods presented here succeed in detecting and locating damage and make time-saving and efficient tools for feature extraction and damage diagnosis.

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