MAPLE: Self-supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
This work addresses the challenge of analyzing high-dimensional data with complex structures, such as biological or image data, but it is incremental as it builds upon UMAP.
The paper tackles the problem of improving nonlinear dimensionality reduction for visual analysis by enhancing UMAP with a self-supervised learning approach, resulting in clearer visual cluster separations and finer subcluster resolution while maintaining comparable computational cost.
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 comparable computational cost.