Deep Sparse Feature Selection and Fusion for Textured Contact Lens Detection

2018 ◽  
Author(s):  
Domenick Poster ◽  
Nasser Nasrabadi ◽  
Benjamin Riggan
Keyword(s):  
Feature Selection ◽  
Contact Lens ◽  
Contact Lens Detection ◽  
Sparse Feature Selection

Group sparse feature selection on local learning based clustering

2016 ◽  
Vol 171 ◽  
pp. 1118-1130 ◽  
Author(s):  
Yue Wu ◽  
Can Wang ◽  
Jiajun Bu ◽  
Chun Chen
Keyword(s):  
Feature Selection ◽  
Local Learning ◽  
Sparse Feature Selection

Nonlinear sparse feature selection algorithm via low matrix rank constraint

2018 ◽  
Vol 78 (23) ◽  
pp. 33319-33337
Author(s):  
Leyuan Zhang ◽  
Yangding Li ◽  
Jilian Zhang ◽  
Pengqing Li ◽  
Jiaye Li
Keyword(s):  
Feature Selection ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Matrix Rank ◽  
Rank Constraint ◽  
Sparse Feature Selection

scholarly journals Machine learning methods in the computational biology of cancer

2014 ◽  
Vol 470 (2167) ◽  
pp. 20140081 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Feature Selection ◽  
Classification Problems ◽  
Open Problems ◽  
Machine Learning Methods ◽  
Selection For ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer ◽  
Sparse Feature Selection

The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.

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