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.

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.

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

Feature Extraction for Kernel Minimum Squared Error by Sparsity Shrinkage

2014 ◽  
Vol 536-537 ◽  
pp. 450-453 ◽  
Author(s):  
Jiang Jiang ◽  
Xi Chen ◽  
Hai Tao Gan
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Optimization Problem ◽  
Subset Selection ◽  
Experimental Results ◽  
Computational Burden ◽  
Squared Error ◽  
Kernel Minimum Squared Error ◽  
Benchmark Datasets ◽  
Training Examples

In this paper, a sparsity based model is proposed for feature selection in kernel minimum squared error (KMSE). By imposing a sparsity shrinkage term, we formulate the procedure of subset selection as an optimization problem. With the chosen small portion of training examples, the computational burden of feature extraction is largely alleviated. Experimental results conducted on several benchmark datasets indicate the effectivity and efficiency of our method.

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

scholarly journals Semi-Supervised Feature Selection with Adaptive Discriminant Analysis

2019 ◽  
Vol 33 ◽  
pp. 10083-10084
Author(s):  
Weichan Zhong ◽  
Xiaojun Chen ◽  
Guowen Yuan ◽  
Yiqin Li ◽  
Feiping Nie
Keyword(s):  
Feature Selection ◽  
Discriminant Analysis ◽  
Iterative Method ◽  
Projection Matrix ◽  
Experimental Results ◽  
Data Sets ◽  
Similarity Matrix ◽  
Selection Methods ◽  
Projected Distance

In this paper, we propose a novel Adaptive Discriminant Analysis for semi-supervised feature selection, namely SADA. Instead of computing fixed similarities before performing feature selection, SADA simultaneously learns an adaptive similarity matrix S and a projection matrix W with an iterative method. In each iteration, S is computed from the projected distance with the learned W and W is computed with the learned S. Therefore, SADA can learn better projection matrix W by weakening the effect of noise features with the adaptive similarity matrix. Experimental results on 4 data sets show the superiority of SADA compared to 5 semisupervised feature selection methods.

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
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