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Sketching the Heat Kernel: Using Gaussian Processes to Embed Data
This paper introduces a novel, non-deterministic method for embedding data in low-dimensional Euclidean space based on computing realizations of a Gaussian process depending on the geometry of the data.
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature
The success of algorithms in the analysis of high-dimensional data is often attributed to the manifold hypothesis, which supposes that this data lie on or near a manifold of much lower dimension.
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