Unsupervised Spectral Feature Selection with Local Structure Learning

2017 ◽  
Author(s):  
Shichao Zhang ◽  
Yue Fang ◽  
Cong Lei ◽  
Yangding Li ◽  
Rongyao Hu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Structure Learning ◽  
Spectral Feature ◽  
Spectral Feature Selection ◽  
Local Structure Learning

Unsupervised feature selection based on local structure learning

2019 ◽  
Vol 79 (45-46) ◽  
pp. 34571-34585
Author(s):  
Yanbei Liu ◽  
Lei Geng ◽  
Fang Zhang ◽  
Jun Wu ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Structure Learning ◽  
Unsupervised Feature Selection ◽  
Local Structure Learning

scholarly journals Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning

2019 ◽  
Vol 3 (2) ◽  
pp. 115 ◽  
Author(s):  
Jiaye Li ◽  
Guoqiu Wen ◽  
Jiangzhang Gan ◽  
Leyuan Zhang ◽  
Shanwen Zhang
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Structure Learning ◽  
Kernel Method ◽  
Optimization Method ◽  
Local Similarity ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Kernel Space ◽  
Local Structure Learning

In this paper, we propose a new unsupervised feature selection algorithm by considering the nonlinear and similarity relationships within the data. To achieve this, we apply the kernel method and local structure learning to consider the nonlinear relationship between features and the local similarity between features. Specifically, we use a kernel function to map each feature of the data into the kernel space. In the high-dimensional kernel space, different features correspond to different weights, and zero weights are unimportant features (e.g. redundant features). Furthermore, we consider the similarity between features through local structure learning, and propose an effective optimization method to solve it. The experimental results show that the proposed algorithm achieves better performance than the comparison algorithm.

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.

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