Unsupervised feature selection based on joint spectral learning and general sparse regression

2019 ◽  
Vol 32 (11) ◽  
pp. 6581-6589
Author(s):  
Tao Chen ◽  
Yanrong Guo ◽  
Shijie Hao
Keyword(s):  
Feature Selection ◽  
Sparse Regression ◽  
Unsupervised Feature Selection ◽  
Spectral Learning

Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection

2014 ◽  
Vol 44 (6) ◽  
pp. 793-804 ◽  
Author(s):  
Chenping Hou ◽  
Feiping Nie ◽  
Xuelong Li ◽  
Dongyun Yi ◽  
Yi Wu
Keyword(s):  
Feature Selection ◽  
Sparse Regression ◽  
Unsupervised Feature Selection ◽  
Joint Embedding

Non-Negative Spectral Learning and Sparse Regression-Based Dual-Graph Regularized Feature Selection

2018 ◽  
Vol 48 (2) ◽  
pp. 793-806 ◽  
Author(s):  
Ronghua Shang ◽  
Wenbing Wang ◽  
Rustam Stolkin ◽  
Licheng Jiao
Keyword(s):  
Feature Selection ◽  
Dual Graph ◽  
Sparse Regression ◽  
Spectral Learning

scholarly journals Subset Selection by Pareto Optimization with Recombination

2020 ◽  
Vol 34 (03) ◽  
pp. 2408-2415
Author(s):  
Chao Qian ◽  
Chao Bian ◽  
Chao Feng
Keyword(s):  
Feature Selection ◽  
Objective Function ◽  
Fundamental Problem ◽  
Subset Selection ◽  
Optimal Solution ◽  
Sparse Regression ◽  
Unsupervised Feature Selection ◽  
Optimal Polynomial ◽  
Approximation Guarantee ◽  
The Given

Subset selection, i.e., to select a limited number of items optimizing some given objective function, is a fundamental problem with various applications such as unsupervised feature selection and sparse regression. By employing a multi-objective evolutionary algorithm (EA) with mutation only to optimize the given objective function and minimize the number of selected items simultaneously, the recently proposed POSS algorithm achieves state-of-the-art performance for subset selection. In this paper, we propose the PORSS algorithm by incorporating recombination, a characterizing feature of EAs, into POSS. We prove that PORSS can achieve the optimal polynomial-time approximation guarantee as POSS when the objective function is monotone, and can find an optimal solution efficiently in some cases whereas POSS cannot. Extensive experiments on unsupervised feature selection and sparse regression show the superiority of PORSS over POSS. Our analysis also theoretically discloses that recombination from diverse solutions can be more likely than mutation alone to generate various variations, thereby leading to better exploration; this may be of independent interest for understanding the influence of recombination.

Cross-view Locality Preserved Diversity and Consensus Learning for Multi-view Unsupervised Feature Selection

2021 ◽  
pp. 1-1
Author(s):  
Chang Tang ◽  
Xiao Zheng ◽  
Xinwang Liu ◽  
Wei Zhang ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Unsupervised Feature Selection
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