MAPLE通过自监督学习提升UMAP的流形建模能力，更好分离复杂数据簇。
MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
- 用自监督学习优化低维流形编码，增强几何结构捕捉能力
- 在生物与图像数据上实现更清晰的聚类分离与亚簇分辨
- 计算成本可控，适合高维复杂数据的可视化分析
我们提出一种新型非线性降维方法MAPLE，通过改进流形建模来增强UMAP性能。MAPLE采用自监督学习策略，更高效地编码低维流形几何结构。其核心是最大流形容量表示（MMCR），通过压缩局部相似点间的方差、放大异质点间方差，有效解耦复杂流形。该方法特别适用于具有显著类内方差和弯曲流形结构的高维数据，如生物或图像数据。定性与定量评估表明，MAPLE在保持可接受计算开销的前提下，相比UMAP能生成更清晰的视觉聚类分离和更精细的子簇分辨。
原文摘要 · Abstract (English)
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.
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