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MAPLE introduces a self-supervised learning framework that refines UMAP by utilizing maximum manifold capacity representations to better model complex, curved data structures. This method specifically targets high-dimensional datasets with significant intra-cluster variance, such as biological or image data, offering improved visual cluster separation and subcluster resolution without incurring prohibitive computational costs. The advancement represents a meaningful step forward in nonlinear dimensionality reduction techniques essential for effective visual analysis of intricate AI-generated or scientific data.
MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
arXiv:2601.20173v3 Announce Type: replace Abstract: We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervised learning approach to more efficiently encode low-dimensional manifold geometry. Central to this approach are maximum manifold capacity representations (MMCRs), which help untangle complex manifolds by compressing variances among locally similar data points while amplifying variance among dissimilar data points. This design is particularly effective for high-dimensional data with substantial intra-cluster variance and curved manifold structures, such as biological or image data. Our qualitative and quantitative evaluations demonstrate that MAPLE can produce clearer visual cluster separations and finer subcluster resolution than UMAP while maintaining tractable computational cost.
Abstract:We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervised learning approach to more efficiently encode low-dimensional manifold geometry. Central to this approach are maximum manifold capacity representations (MMCRs), which help untangle complex manifolds by compressing variances among locally similar data points while amplifying variance among dissimilar data points. This design is particularly effective for high-dimensional data with substantial intra-cluster variance and curved manifold structures, such as biological or image data. Our qualitative and quantitative evaluations demonstrate that MAPLE can produce clearer visual cluster separations and finer subcluster resolution than UMAP while maintaining tractable computational cost.
Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
arXiv:2601.20173 [cs.LG]
(or arXiv:2601.20173v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.20173 Focus to learn more arXiv-issued DOI via DataCite
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