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Dimensionality reduction
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Dimensionality reduction
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convex programming
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Semisupervised Dual-Geometric Subspace Projection for Dimensionality
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computational complexity
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supervised dimensionality reduction of local features.
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data visualisation, feature extraction, image representation,
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locality preserving
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Machine learning, Semi-supervised learning, Active learning,
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Semi-supervised discriminant embedding (SDE),
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2208
Dimensionality reduction,
Semi-supervised discriminant analysis, Incremental learning,
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Chen, H.[Hong],
Nie, F.P.[Fei-Ping],
Wang, R.[Rong],
Li, X.L.[Xue-Long],
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2112
Principal component analysis, Manifolds, Manifold learning,
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Xie, L.P.[Li-Ping],
Guo, W.[Weili],
Wei, H.[Haikun],
Tang, Y.Y.[Yuan-Yan],
Tao, D.C.[Da-Cheng],
Efficient Unsupervised Dimension Reduction for Streaming Multiview
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2203
Dimensionality reduction, Correlation, Memory management,
Prediction algorithms, Optimization, Training, Data models,
unsupervised multiview dimension reduction (UMDR)
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Yuan, Z.[Zhong],
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2205
Fuzzy rough set theory, Unsupervised attribute reduction,
Complementary entropy, Maximal information, Mixed data
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Lu, X.H.[Xiao-Huan],
Long, J.[Jiang],
Wen, J.[Jie],
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unsupervised dimensionality reduction,
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Elsevier DOI
2208
Dimensionality reduction, Feature extraction, Graph embedding,
Unsupervised learning
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Wang, J.[Jikui],
Wu, Y.W.[Yi-Wen],
Li, B.[Bing],
Yang, Z.G.[Zhen-Guo],
Nie, F.P.[Fei-Ping],
Fast anchor graph preserving projections,
PR(146), 2024, pp. 109996.
Elsevier DOI Code:
WWW Link.
2311
Dimensionality reduction, Principal component analysis,
Anchor graph, Unsupervised learning
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Wang, Q.[Quan],
Wang, F.[Fei],
Li, Z.H.[Zhong-Heng],
Wang, Z.[Zheng],
Nie, F.P.[Fei-Ping],
Coordinate Descent Optimized Trace Difference Model for Joint
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PR(146), 2024, pp. 110062.
Elsevier DOI
2311
Clustering, Coordinate descent method, Feature extraction,
Trace difference criterion, Unsupervised learning
BibRef
Yang, Q.W.[Qian-Wen],
Sun, F.C.[Fu-Chun],
Unsupervised Local Linear Preserving Manifold Reduction with
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Springer DOI
1711
Manifold learning.
BibRef
Wang, Y.M.[Ya-Ming],
Morariu, V.I.[Vlad I.],
Davis, L.S.[Larry S.],
Unsupervised Feature Extraction Inspired by Latent Low-Rank
Representation,
WACV15(542-549)
IEEE DOI
1503
Algorithm design and analysis
BibRef
Morariu, V.I.[Vlad I.],
Ahmed, E.[Ejaz],
Santhanam, V.[Venkataraman],
Harwood, D.[David],
Davis, L.S.[Larry S.],
Composite Discriminant Factor analysis,
WACV14(564-571)
IEEE DOI
1406
Accuracy
BibRef
Carreira-Perpinan, M.A.[Miguel A.],
Lu, Z.D.[Zheng-Dong],
Parametric dimensionality reduction by unsupervised regression,
CVPR10(1895-1902).
IEEE DOI
1006
BibRef
Earlier:
Dimensionality reduction by unsupervised regression,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Yang, W.[Wuyi],
Zhang, S.W.[Shu-Wu],
Liang, W.[Wei],
A Graph Based Subspace Semi-supervised Learning Framework for
Dimensionality Reduction,
ECCV08(II: 664-677).
Springer DOI
0810
BibRef
Gong, H.F.[Hai-Feng],
Pan, C.H.[Chun-Hong],
Yang, Q.[Qing],
Lu, H.Q.[Han-Qing],
Ma, S.D.[Song-De],
Neural Network Modeling of Spectral Embedding,
BMVC06(I:227).
PDF File.
0609
BibRef
Earlier:
A Semi-Supervised Framework for Mapping Data to the Intrinsic Manifold,
ICCV05(I: 98-105).
IEEE DOI
0510
Reduce dimensionality, but to the intrinsic form.
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Riemannian Manifold Learning, Grassman Manifold Clustering .