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Tangent Space and Dimension Estimation with the Wasserstein Distance
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Vector diffusion maps and the connection Laplacian
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Nonlinear Dimensionality Reduction
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Introduction to Riemannian Manifolds
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Optimal Transport: Old and New
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Finding the Homology of Submanifolds with High Confidence from Random Samples
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Stability and Minimax Optimality of Tangential Delaunay Complexes for Manifold Reconstruction
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Recovering the Homology of Immersed Manifolds
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User-Friendly Tail Bounds for Sums of Random Matrices
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A tutorial on spectral clustering
inferredcites →https://doi.org/10.1016/j.acha.2018.03.003openalex-snapshot
The shape of data and probability measures
inferredcites →https://doi.org/10.1016/j.patcog.2023.110030openalex-snapshot
Adaptive local Principal Component Analysis improves the clustering of high-dimensional data
inferredcites →https://doi.org/10.1021/ie990110iopenalex-snapshot
Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods
inferredcites →https://doi.org/10.1093/biomet/asv008openalex-snapshot
A useful variant of the Davis–Kahan theorem for statisticians
inferredcites →https://doi.org/10.1093/imaiai/iat003openalex-snapshot
Tangent space estimation for smooth embeddings of Riemannian manifolds
inferredcites →https://doi.org/10.1093/imaiai/iau004openalex-snapshot
Non-asymptotic analysis of tangent space perturbation
inferredcites →https://doi.org/10.1109/t-c.1971.223208openalex-snapshot
An Algorithm for Finding Intrinsic Dimensionality of Data
inferredcites →https://doi.org/10.1137/0707001openalex-snapshot
The Rotation of Eigenvectors by a Perturbation. III
inferredcites →https://doi.org/10.1137/s1064827502419154openalex-snapshot
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
inferredcites →https://doi.org/10.1142/9789812796936_0011openalex-snapshot
THE VARIATION OF THE SPECTRUM OF A NORMAL MATRIX
inferredcites →https://doi.org/10.1146/annurev-statistics-031017-100045openalex-snapshot
Topological Data Analysis
inferredcites →https://doi.org/10.1162/neco.1997.9.7.1493openalex-snapshot
Dimension Reduction by Local Principal Component Analysis
inferredcites →https://doi.org/10.1214/12-aos994openalex-snapshot
Manifold estimation and singular deconvolution under Hausdorff loss
inferredcites →https://doi.org/10.1214/19-aos1839openalex-snapshot
Nonasymptotic upper bounds for the reconstruction error of PCA
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Estimating the reach of a manifold
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The variation of the spectrum of a normal matrix
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Concentration inequalities and moment bounds for sample covariance operators
inferredcites →https://doi.org/10.3389/fnins.2019.01066openalex-snapshot
Denoising High-Field Multi-Dimensional MRI With Local Complex PCA
inferredcites →https://doi.org/10.48550/arxiv.1301.2007openalex-snapshot
Spectral Clustering Based on Local PCA
inferredcites →https://doi.org/10.48550/arxiv.1710.04019openalex-snapshot
Une introduction à l'analyse topologique des données pour data scientist : aspects fondamentaux et pratiques
inferredcites →https://doi.org/10.48550/arxiv.1802.03426openalex-snapshot
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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LieDetect: Detection of Representation Orbits of Compact Lie Groups From Point Clouds
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Task-free continual learning with expansion-based granular CNN: Gradual partitioning of manifolds in image stream classification
inferred← citeshttps://doi.org/10.1038/s43588-026-00970-6openalex-snapshot
Feature-preserving manifold approximation and projection to analyze single-cell data
inferred← citeshttps://doi.org/10.1137/23m1575135openalex-snapshot
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature
inferred← citeshttps://doi.org/10.1137/24m1665763openalex-snapshot
HADES: Fast Singularity Detection with Local Measure Comparison
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