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Dimensionality Reduction

Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms
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AI Generated Machine Learning Dimensionality Reduction 🏢 Rutgers University
Neuc-MDS: Revolutionizing multidimensional scaling by using bilinear forms for non-Euclidean data, minimizing errors, and resolving the dimensionality paradox!
Navigating the Effect of Parametrization for Dimensionality Reduction
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Machine Learning Dimensionality Reduction 🏢 Duke University
ParamRepulsor, a novel parametric dimensionality reduction method, achieves state-of-the-art local structure preservation by mining hard negatives and using a tailored loss function.
Contrastive dimension reduction: when and how?
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Machine Learning Dimensionality Reduction 🏢 University of North Carolina at Chapel Hill
This research introduces a hypothesis test and a contrastive dimension estimator to identify unique foreground information in contrastive datasets, advancing the field of dimension reduction.