scholarly journals Erratum to: Non-global structure of the O α s 2 $$ \mathcal{O}\left({\alpha}_s^2\right) $$ dijet soft function

2017 ◽  
Vol 2017 (10) ◽  
Author(s):  
Andrew Hornig ◽  
Christopher Lee ◽  
Iain W. Stewart ◽  
Jonathan R. Walsh ◽  
Saba Zuberi
Keyword(s):  
Global Structure ◽  
Soft Function

scholarly journals Non-global structure of the $ \mathcal{O}\left( {\alpha_s^2} \right) $ dijet soft function

2011 ◽  
Vol 2011 (8) ◽  
Author(s):  
Andrew Hornig ◽  
Christopher Lee ◽  
Iain W. Stewart ◽  
Jonathan R. Walsh ◽  
Saba Zuberi
Keyword(s):  
Global Structure ◽  
Soft Function

scholarly journals An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3627
Author(s):  
Bo Jin ◽  
Chunling Fu ◽  
Yong Jin ◽  
Wei Yang ◽  
Shengbin Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Gene Selection ◽  
Dimensional Space ◽  
Original Data ◽  
Global Structure ◽  
Biological Data ◽  
Special Treatment ◽  
Selection Scheme ◽  
Unsupervised Feature Selection

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.

The global structure of the annual and semiannual sea surface height variability from Geosat altimeter data

1992 ◽  
Vol 97 (C11) ◽  
pp. 17813-17828 ◽  
Author(s):  
Gregg A. Jacobs ◽  
George H. Born ◽  
Mike E. Parke ◽  
Patrick C. Allen
Keyword(s):  
Sea Surface Height ◽  
Global Structure ◽  
Sea Surface ◽  
Altimeter Data

Local and global structure preserving hashing for fast digital fingerprint tracing

2014 ◽  
Vol 74 (18) ◽  
pp. 8003-8023 ◽  
Author(s):  
Cong Liu ◽  
Hefei Ling ◽  
Fuhao Zou ◽  
Yunfei Wang ◽  
Hui Feng ◽  
...  
Keyword(s):  
Global Structure ◽  
Structure Preserving ◽  
Digital Fingerprint
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