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Found 5 papers, 2 papers with code
Manifold learning: what, how, and why
Manifold learning (ML), known also as non-linear dimension reduction, is a set of methods to find the low dimensional structure of data.
Distribution free optimality intervals for clustering
We demonstrate the practical relevance of this method by obtaining guarantees for the K-means and the Normalized Cut clustering criteria on realistic data sets.
The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian
The study of the null space embedding of the graph Laplacian $\mathbf{\mathcal L}_0$ has spurred new research and applications, such as spectral clustering algorithms with theoretical guarantees and estimators of the Stochastic Block Model.
Helmholtzian Eigenmap: Topological feature discovery & edge flow learning from point cloud data
In this work, we propose the estimation of the manifold Helmholtzian from point cloud data by a weighted 1-Laplacian $\mathcal L_1$.
Selecting the independent coordinates of manifolds with large aspect ratios
Many manifold embedding algorithms fail apparently when the data manifold has a large aspect ratio (such as a long, thin strip).
