A graph-based technique splits ambiguous instances into multiple points in DR projections to reduce partial neighborhood embedding and reveal hidden memberships.
M., Kerren A., Hirata N
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 4years
2026 4verdicts
UNVERDICTED 4roles
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CADI quantifies the preservation of relative cluster angles in low-dimensional projections using internal angles from point triples.
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
Adapting SGD from graph drawing produces a scikit-learn compatible stochastic solver that converges faster than SMACOF for global stress minimization while achieving comparable or lower stress on benchmarks.
citing papers explorer
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When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting
A graph-based technique splits ambiguous instances into multiple points in DR projections to reduce partial neighborhood embedding and reveal hidden memberships.
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Class Angular Distortion Index for Dimensionality Reduction
CADI quantifies the preservation of relative cluster angles in low-dimensional projections using internal angles from point triples.
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MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
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Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization
Adapting SGD from graph drawing produces a scikit-learn compatible stochastic solver that converges faster than SMACOF for global stress minimization while achieving comparable or lower stress on benchmarks.