Unsupervised Feature Selection
Unsupervised Feature Selection is a feature selection paradigm that identifies and retains the most relevant subset of features from high-dimensional data without relying on labeled outcomes, typically by leveraging intrinsic data structures, statistical dependencies, or geometric properties to preserve maximal information or discriminative capacity inherent in the unlabeled data.
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Publications per year
2017–2026
777
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1.4K
A survey on feature selection methods
Girish Chandrashekar, Ferat Sahin · Computers & Electrical Engineering · 2013 · 5.1K citations
A Comparative Study on Feature Selection in Text Categorization
Yiming Yang, Jan Pedersen · 1997 · 4.8K citations
Feature selection in machine learning: A new perspective
Jie Cai, Jiawei Luo, Shulin Wang et al. · Neurocomputing · 2018 · 2.1K citations
Laplacian Score for Feature Selection
Xiaofei He, Deng Cai, Partha Niyogi · 2005 · 1.8K citations
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Northwestern Polytechnical University
Xi'an, China
29 h-index
124 publications
7.8K citations

Chinese Academy of Sciences
Beijing, China
28 h-index
78 publications
8K citations

University of Electronic Science and Technology of China
Chengdu, China
20 h-index
47 publications
4K citations
Arizona State University
Tempe, United States
18 h-index
40 publications
6.1K citations
Tsinghua University
Beijing, China
17 h-index
44 publications
1.4K citations

Southwest Jiaotong University
Chengdu, China
17 h-index
56 publications
3.8K citations

Indian Statistical Institute
Kolkata, India
16 h-index
32 publications
6.1K citations
Chuo University
Hachiōji, Japan
14 h-index
21 publications
712 citations
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58 publications · 4.9K citations
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46 publications · 4.2K citations
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45 publications · 4K citations
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TKDE · 27 publications · 2.4K citations
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