Integrating Constraints Into Dimensionality Reduction for Visualization: A Survey
Author:
Affiliation:
1. University of Namur, Namur, Belgium
Funder
EOS-VeriLearn
Fonds De La Recherche Scientifique – FNRS
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Science Applications,Artificial Intelligence
Link
http://xplorestaging.ieee.org/ielx7/9078688/9960713/09878189.pdf?arnumber=9878189
Reference143 articles.
1. Pairwise Constraints-Guided Dimensionality Reduction
2. Knowledge driven dimension reduction for clustering;davidson;Proc Int Joint Conf Artif Intell,0
3. Sparse multidimensional scaling using landmark points;silva,2004
4. Multidimensional scaling: I. Theory and method
5. Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition
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1. ParaDime: A Framework for Parametric Dimensionality Reduction;Computer Graphics Forum;2023-06
2. Class-constrained t-SNE: Combining Data Features and Class Probabilities;IEEE Transactions on Visualization and Computer Graphics;2023
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