Linear And Nonlinear Feature Extraction Methods Applied To Automatic Classification Of Proliferating Cells

1982 ◽  
Author(s):  
W. Hobel ◽  
W. Abmayr ◽  
S. J. Poppl ◽  
W. Giaretti ◽  
P. Dormer
Keyword(s):  
Feature Extraction ◽  
Automatic Classification ◽  
Extraction Methods ◽  
Proliferating Cells ◽  
Linear And Nonlinear ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

scholarly journals Target Recognition of SAR Images Based on Linear and Nonlinear Feature Extraction and Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Haiyan Zhao
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Principal Component ◽  
Kernel Functions ◽  
Support Vector ◽  
Sar Images ◽  
Feature Vectors ◽  
Linear And Nonlinear ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

A synthetic aperture radar (SAR) target recognition method combining linear and nonlinear feature extraction and classifiers is proposed. The principal component analysis (PCA) and kernel PCA (KPCA) are used to extract feature vectors of the original SAR image, respectively, which are classical and reliable feature extraction algorithms. In addition, KPCA can effectively make up for the weak linear description ability of PCA. Afterwards, support vector machine (SVM) and kernel sparse representation-based classification (KSRC) are used to classify the KPCA and PCA feature vectors, respectively. Similar to the idea of feature extraction, KSRC mainly introduces kernel functions to improve the processing and classification capabilities of nonlinear data. Through the combination of linear and nonlinear features and classifiers, the internal data structure of SAR images and the correspondence between test and training samples can be better investigated. In the experiment, the performance of the proposed method is tested based on the MSTAR dataset. The results show the effectiveness and robustness of the proposed method.

Nonlinear feature extraction for objective classification of complex auditory brainstem responses to diotic perceptually critical consonant-vowel syllables

2016 ◽  
Vol 43 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Amir Salar Jafarpisheh ◽  
Amir Homayoun Jafari ◽  
Mohammadjavad Abolhassani ◽  
Mohammad Farhadi ◽  
Hamed Sadjedi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Auditory Brainstem ◽  
Auditory Brainstem Responses ◽  
Objective Classification ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature
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