New research details minimax rates for multi-manifold clustering

A new research paper published on arXiv details minimax rates and improved spectral convergence for multi-manifold clustering. The study focuses on estimating eigenpairs for a tensorized, density-weighted Laplace-Beltrami operator using data sampled from a multi-manifold mixture distribution. Researchers established the minimax rate for approximating eigenvalues and eigenfunctions, and analyzed a graph Laplacian estimator, showing improved spectral convergence rates for multi-manifold learning. AI

IMPACT This research provides theoretical insights into multi-manifold spectral estimation, potentially improving clustering algorithms.

RANK_REASON The cluster contains a research paper detailing theoretical advancements in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• graph Laplacian estimator
• kernel density estimation
• Laplace--Beltrami operator
• multi-manifold mixture distribution

• paper
• other

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