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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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
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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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.