Joint Subspace Learning and Sparse Regression for Feature Selection in Kernel Space

2018 ◽  
pp. 147-158 ◽  
Author(s):  
Long Chen ◽  
Zhi Zhong
Keyword(s):  
Feature Selection ◽  
Subspace Learning ◽  
Kernel Space ◽  
Sparse Regression

scholarly journals Subspace learning-based graph regularized feature selection

2016 ◽  
Vol 112 ◽  
pp. 152-165 ◽  
Author(s):  
Ronghua Shang ◽  
Wenbing Wang ◽  
Rustam Stolkin ◽  
Licheng Jiao
Keyword(s):  
Feature Selection ◽  
Subspace 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 Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection

2020 ◽  
Vol 121 ◽  
pp. 441-451 ◽  
Author(s):  
Imran Razzak ◽  
Raghib Abu Saris ◽  
Michael Blumenstein ◽  
Guandong Xu
Keyword(s):  
Feature Selection ◽  
Subspace Learning

Subspace learning for unsupervised feature selection via matrix factorization

2015 ◽  
Vol 48 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Shiping Wang ◽  
Witold Pedrycz ◽  
Qingxin Zhu ◽  
William Zhu
Keyword(s):  
Feature Selection ◽  
Matrix Factorization ◽  
Subspace Learning ◽  
Unsupervised Feature Selection

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.

Agent-Based RBF Network Classifier with Feature Selection in a Kernel Space

2016 ◽  
Vol 47 (1-2) ◽  
pp. 17-31 ◽  
Author(s):  
Ireneusz Czarnowski ◽  
Piotr Jędrzejowicz
Keyword(s):  
Feature Selection ◽  
Kernel Space ◽  
Agent Based ◽  
Rbf Network
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