Beyond Redundancies: A Metric-Invariant Method for Unsupervised Feature Selection

2010 ◽  
Vol 22 (3) ◽  
pp. 348-364 ◽  
Author(s):  
Yuexian Hou ◽  
Peng Zhang ◽  
Tingxu Yan ◽  
Wenjie Li ◽  
Dawei Song
Keyword(s):  
Feature Selection ◽  
Unsupervised Feature Selection ◽  
Invariant Method ◽  
Metric Invariant

Unsupervised Feature Selection With Extended OLSDA via Embedding Nonnegative Manifold Structure

2021 ◽  
pp. 1-7
Author(s):  
Rui Zhang ◽  
Hongyuan Zhang ◽  
Xuelong Li ◽  
Sheng Yang
Keyword(s):  
Feature Selection ◽  
Unsupervised Feature Selection ◽  
Manifold Structure

scholarly journals Unsupervised Feature Selection using Pseudo Label Approximation

2021 ◽  
Author(s):  
Ren Deng ◽  
Ye Liu ◽  
Liyan Luo ◽  
DongJing Chen ◽  
Xijie Li
Keyword(s):  
Feature Selection ◽  
Unsupervised Feature Selection

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

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.

Unsupervised Feature Selection via Multi-step Markov Probability Relationship

2021 ◽  
Author(s):  
Yan Min ◽  
Mao Ye ◽  
Liang Tian ◽  
Yulin Jian ◽  
Ce Zhu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Unsupervised Feature Selection

Unsupervised feature selection by non-convex regularized self-representation

2021 ◽  
Vol 173 ◽  
pp. 114643
Author(s):  
Jianyu Miao ◽  
Yuan Ping ◽  
Zhensong Chen ◽  
Xiao-Bo Jin ◽  
Peijia Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Unsupervised Feature Selection

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
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