Unsupervised Spectral Feature Selection with Dynamic Hyper-graph Learning

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Xiaofeng Zhu ◽  
Shichao Zhang ◽  
Yonghua Zhu ◽  
Pengfei Zhu ◽  
Yue Gao
Keyword(s):  
Feature Selection ◽  
Spectral Feature ◽  
Graph Learning ◽  
Spectral Feature Selection

Dynamic graph learning for spectral feature selection

2017 ◽  
Vol 77 (22) ◽  
pp. 29739-29755 ◽  
Author(s):  
Wei Zheng ◽  
Xiaofeng Zhu ◽  
Yonghua Zhu ◽  
Rongyao Hu ◽  
Cong Lei
Keyword(s):  
Feature Selection ◽  
Spectral Feature ◽  
Dynamic Graph ◽  
Graph Learning ◽  
Spectral Feature Selection

LIBS development methodology for forensic nuclear materials analysis

2018 ◽  
Vol 10 (7) ◽  
pp. 791-798 ◽  
Author(s):  
Bobby Bhatt ◽  
Kalambuka Hudson Angeyo ◽  
Alix Dehayem-Kamadjeu
Keyword(s):  
Feature Selection ◽  
Spectral Feature ◽  
Forensic Analysis ◽  
Nuclear Materials ◽  
Methodology Development ◽  
Materials Analysis ◽  
Development Methodology ◽  
Selection For ◽  
Spectral Feature Selection ◽  
Nuclear Forensic

Methodology development of LIBS coupled with chemometrics utilizing weak U-lines and spectral feature selection for rapid nuclear forensic analysis.

Genetic algorithm spectral feature selection coupled with quadratic discriminant analysis for ATR-FTIR spectrometric diagnosis of basal cell carcinoma via blood sample analysis

RSC Advances ◽  
2014 ◽  
Vol 4 (78) ◽  
pp. 41484-41490 ◽  
Author(s):  
Mohammadreza Khanmohammadi ◽  
Keyvan Ghasemi ◽  
Amir Bagheri Garmarudi
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Spectral Feature ◽  
Quadratic Discriminant Analysis ◽  
Sample Analysis ◽  
Blood Samples ◽  
Spectral Feature Selection

A diagnostic approach for basal cell carcinoma (BCC) has been developed based on investigation of the infrared spectra of blood samples.

Sign in / Sign up

Export Citation Format

Share Document