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Found 3 papers, 3 papers with code
Data segmentation based on the local intrinsic dimension
Here we develop a robust approach to discriminate regions with different local IDs and segment the points accordingly.
Estimating the intrinsic dimension of datasets by a minimal neighborhood information
Analyzing large volumes of high-dimensional data is an issue of fundamental importance in data science, molecular simulations and beyond.
Automatic topography of high-dimensional data sets by non-parametric Density Peak clustering
The approach is based on an unsupervised extension of Density Peak clustering and a non-parametric density estimator that measures the probability density in the manifold containing the data.
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