Joint self-representation and subspace learning for unsupervised feature selection

2017 ◽  
Vol 21 (6) ◽  
pp. 1745-1758 ◽  
Author(s):  
Ruili Wang ◽  
Ming Zong
Keyword(s):  
Feature Selection ◽  
Subspace Learning ◽  
Unsupervised Feature Selection

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

Discriminative sparse subspace learning and its application to unsupervised feature selection

2016 ◽  
Vol 61 ◽  
pp. 104-118 ◽  
Author(s):  
Nan Zhou ◽  
Hong Cheng ◽  
Witold Pedrycz ◽  
Yong Zhang ◽  
Huaping Liu
Keyword(s):  
Feature Selection ◽  
Subspace Learning ◽  
Unsupervised Feature Selection

Nonnegative Laplacian embedding guided subspace learning for unsupervised feature selection

2019 ◽  
Vol 93 ◽  
pp. 337-352 ◽  
Author(s):  
Yong Zhang ◽  
Qi Wang ◽  
Dun-wei Gong ◽  
Xian-fang Song
Keyword(s):  
Feature Selection ◽  
Subspace Learning ◽  
Unsupervised Feature Selection

Unsupervised feature selection by combining subspace learning with feature self-representation

2018 ◽  
Vol 109 ◽  
pp. 35-43 ◽  
Author(s):  
Yangding Li ◽  
Cong Lei ◽  
Yue Fang ◽  
Rongyao Hu ◽  
Yonggang Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Subspace Learning ◽  
Unsupervised Feature Selection

scholarly journals Adaptive Hypergraph Learning for Unsupervised Feature Selection

2017 ◽  
Author(s):  
Xiaofeng Zhu ◽  
Yonghua Zhu ◽  
Shichao Zhang ◽  
Rongyao Hu ◽  
Wei He
Keyword(s):  
Feature Selection ◽  
Original Data ◽  
Subspace Learning ◽  
Simple Graph ◽  
Experimental Results ◽  
Similarity Matrix ◽  
Unsupervised Feature Selection ◽  
Feature Spaces ◽  
Hypergraph Learning ◽  
Benchmark Datasets

Current unsupervised feature selection (UFS) methods learn the similarity matrix by using a simple graph which is learnt from the original data as well as is independent from the process of feature selection, and thus unable to efficiently remove the redundant/irrelevant features. To address these issues, we propose a new UFS method to jointly learn the similarity matrix and conduct both subspace learning (via learning a dynamic hypergraph) and feature selection (via a sparsity constraint). As a result, we reduce the feature dimensions using different methods (i.e., subspace learning and feature selection) from different feature spaces, and thus makes our method select the informative features effectively and robustly. We tested our method using benchmark datasets to conduct the clustering tasks using the selected features, and the experimental results show that our proposed method outperforms all the comparison methods.

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