AI RESEARCH
A Comparative Study of UMAP and Other Dimensionality Reduction Methods
arXiv CS.LG
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ArXi:2603.02275v2 Announce Type: replace 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.