A Comparative Study of UMAP and Other Dimensionality Reduction Methods
Uniform Manifold Approximation and Projection (UMAP) is a widely used manifold learning technique for dimensionality reduction. This paper studies UMAP, supervised UMAP, and several competing dimensionality reduction methods, including Principal Component Analysis (PCA), Kernel PCA, Sliced Inverse Regression (SIR), Kernel SIR, and t-distributed Stochastic Neighbor Embedding, through a comprehensive comparative analysis. Although UMAP has attracted substantial attention for preserving local and global structures, its supervised extensions, particularly for regression settings, remain rather underexplored. We provide a systematic evaluation of supervised UMAP for both regression and classif...
| Authors | Guanzhe Zhang, Shanshan Ding, Zhezhen Jin |
|---|---|
| Primary category | cs.LG |
| Published | 1 March 2026 |
| Last updated at source | 30 April 2026 |
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