UMAP dimensionality reduction method compared to PCA and t-SNE

A new paper compares Uniform Manifold Approximation and Projection (UMAP) with other dimensionality reduction techniques like PCA and t-SNE. The study systematically evaluates supervised UMAP for both regression and classification tasks using simulated and real-world datasets. Results indicate that while supervised UMAP is effective for classification, it struggles to incorporate response information for regression, suggesting an area for future research. AI

IMPACT Provides a comparative analysis of dimensionality reduction methods, highlighting limitations in supervised UMAP for regression tasks.

RANK_REASON Academic paper comparing dimensionality reduction techniques.

Read on arXiv cs.LG →

• Guanzhe Zhang
• PCA
• t-SNE
• UMAP

• paper
• other

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