Semi-supervised Feature Learning
Semi-Supervised Feature Learning is a machine learning paradigm that leverages both labeled and unlabeled data to learn feature representations by exploiting the underlying data structure, distinguishing itself from purely supervised or unsupervised approaches through the joint use of label information and data distribution assumptions.
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23.1K
Citations
1.6K
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Institutions
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2017–2026
280
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424
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Zhirong Wu, Yuanjun Xiong, Stella X. Yu et al. · 2018 · 3.6K citations
An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition
Minnan Luo, Xiaojun Chang, Liqiang Nie et al. · IEEE Transactions on Cybernetics · 2017 · 331 citations
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima et al. · Machine Learning · 2009 · 261 citations · Full text
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Chinese Academy of Sciences
Beijing, China
17 h-index
67 publications
2.9K citations

Xidian University
Xi'an, China
11 h-index
39 publications
1.2K citations
Carnegie Mellon University
Pittsburgh, United States
10 h-index
19 publications
2.4K citations
Tsinghua University
Beijing, China
10 h-index
29 publications
2K citations

University of Electronic Science and Technology of China
Chengdu, China
9 h-index
27 publications
1.7K citations
Tongji University
Shanghai, China
9 h-index
26 publications
731 citations

Northwestern Polytechnical University
Xi'an, China
8 h-index
13 publications
371 citations
University of Chinese Academy of Sciences
Beijing, China
8 h-index
26 publications
786 citations
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Repository · 18 publications · 632 citations
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12 publications · 694 citations
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12 publications · 490 citations
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