Nonlinear feature extraction methods for removing temperature effects in multi-mode guided-waves in pipes

2015 ◽  
Author(s):  
Matineh Eybpoosh ◽  
Mario Berges ◽  
Hae Young Noh
Keyword(s):  
Feature Extraction ◽  
Temperature Effects ◽  
Guided Waves ◽  
Extraction Methods ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature ◽  
Multi Mode

ALGORITHM FOR THE DETECTION OF CONGESTIVE HEART FAILURE INDEX

2017 ◽  
Vol 17 (07) ◽  
pp. 1740043 ◽  
Author(s):  
YI DA KANG ◽  
DEMING ZHUO ◽  
RUI EN ANNE FOO ◽  
CHOO MIN LIM ◽  
OLIVER FAUST ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Computer Support ◽  
Electrocardiogram Signals ◽  
Nonlinear Features ◽  
Index Value ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

This study documents our efforts to provide computer support for the diagnosis of congestive heart failure (CHF). That computer support takes the form of an index value. A high index value indicates a low probability of CHF, and an index value below a threshold of 25.6 suggests a high probability of CHF. To create that index, we have designed a sophisticated algorithm chain which takes electrocardiogram signals as input. The signals are pre-processed before they are sent to a range of nonlinear feature extraction algorithms. The top 10 feature extraction methods were used to create the CHF index. By using objective feature extraction algorithms, we avoid the problem of inter- and intra-observer variability. We observed that the nonlinear feature extraction methods reflect the nature of the human heart very well. That observation is based on the fact that the nonlinear features achieved low [Formula: see text]-values and high feature ranking criterion scores.

scholarly journals A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components

2014 ◽  
Vol 51 (1) ◽  
pp. 57-73 ◽  
Author(s):  
Karol Deręgowski ◽  
Mirosław Krzyśko
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Principal Components ◽  
Learning Algorithm ◽  
Extraction Methods ◽  
Kernel Trick ◽  
New Learning ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

SUMMARY Kernel principal components (KPC) and kernel discriminant coordinates (KDC), which are the extensions of principal components and discriminant coordinates, respectively, from a linear domain to a nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods. The kernel discriminant coordinates space has proven to be a very powerful space for pattern recognition. However, further study shows that there are still drawbacks in this method. To improve the performance of pattern recognition, we propose a new learning algorithm combining the advantages of KPC and KDC

Sign in / Sign up

Export Citation Format

Share Document