scholarly journals Non-Linear Feature Extraction by Linear Principal Component Analysis Using Local Kernel

10.5772/9367 ◽  
2010 ◽  
Author(s):  
Kazuhiro Hotta
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Feature ◽  
Linear Feature Extraction ◽  
Non Linear

scholarly journals Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2748
Author(s):  
Jersson X. Leon-Medina ◽  
Maribel Anaya ◽  
Núria Parés ◽  
Diego A. Tibaduiza ◽  
Francesc Pozo
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Wind Turbine ◽  
Structural Damage ◽  
Principal Component ◽  
Gradient Boosting ◽  
Damage Classification ◽  
Linear Feature ◽  
Linear Feature Extraction ◽  
Extreme Gradient Boosting

Damage classification is an important topic in the development of structural health monitoring systems. When applied to wind-turbine foundations, it provides information about the state of the structure, helps in maintenance, and prevents catastrophic failures. A data-driven pattern-recognition methodology for structural damage classification was developed in this study. The proposed methodology involves several stages: (1) data acquisition, (2) data arrangement, (3) data normalization through the mean-centered unitary group-scaling method, (4) linear feature extraction, (5) classification using the extreme gradient boosting machine learning classifier, and (6) validation applying a 5-fold cross-validation technique. The linear feature extraction capabilities of principal component analysis are employed; the original data of 58,008 features is reduced to only 21 features. The methodology is validated with an experimental test performed in a small-scale wind-turbine foundation structure that simulates the perturbation effects caused by wind and marine waves by applying an unknown white noise signal excitation to the structure. A vibration-response methodology is selected for collecting accelerometer data from both the healthy structure and the structure subjected to four different damage scenarios. The datasets are satisfactorily classified, with performance measures over 99.9% after using the proposed damage classification methodology.

scholarly journals DNN Transfer Learning Based Non-Linear Feature Extraction for Acoustic Event Classification

2017 ◽  
Vol E100.D (9) ◽  
pp. 2249-2252 ◽  
Author(s):  
Seongkyu MUN ◽  
Minkyu SHIN ◽  
Suwon SHON ◽  
Wooil KIM ◽  
David K. HAN ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Event Classification ◽  
Acoustic Event ◽  
Linear Feature ◽  
Linear Feature Extraction ◽  
Non Linear

scholarly journals Effects of Collagen–Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4117
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Cell Lines ◽  
Time Course ◽  
Expression Profiles ◽  
Principal Component ◽  
Component Analysis ◽  
Altered Expression ◽  
Unsupervised Feature Extraction

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

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