Dimensionality Reduction For Segmentation
Dimensionality Reduction for Segmentation is a methodological framework in machine learning that reduces the number of input features while preserving structurally relevant patterns to enhance the accuracy and efficiency of image or data segmentation tasks.
34
Publications
1.8K
Citations
103
Authors
56
Institutions
Publications per year
2017–2026
14
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Motion segmentation by subspace separation and model selection
Kenichi Kanatani · 2002 · 355 citations · Full text
Clustering and dimensionality reduction on Riemannian manifolds
Alvina Goh, René Vidal · 2008 · 125 citations
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Johns Hopkins University
Baltimore, United States
2 h-index
4 publications
644 citations

Université de Reims Champagne-Ardenne
Reims, France
2 h-index
2 publications
26 citations
Koneru Lakshmaiah Education Foundation
Vijayawada, India
2 h-index
3 publications
61 citations
Shandong University of Science and Technology
Qingdao, China
2 h-index
8 publications
192 citations
Instituto de Engenharia de Sistemas e Computadores Microsistemas e Nanotecnologias
Lisbon, Portugal
1 h-index
1 publications
18 citations
Carnegie Mellon University
Pittsburgh, United States
1 h-index
3 publications
36 citations

Tanta University
Tanta, Egypt
1 h-index
1 publications
104 citations
Xinjiang University
Ürümqi, China
1 h-index
4 publications
52 citations
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IJCIS · 2 publications · 48 citations
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Repository · 1 publications · 40 citations
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Repository · 1 publications · 18 citations
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1 publications · 13 citations
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JASMS · 1 publications · 24 citations
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