ArticleScientific reports2017
Estimating the intrinsic dimension of datasets by a minimal neighborhood information.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Using Latent Representations to Link Disjoint Longitudinal Data for Mixed-Effects Regression.Statistics in medicine · 2026Article
- Contrastive learning to fine-tune feature extraction models for the visual cortex.PLoS computational biology · 2026Article
- Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification.Scientific reports · 2026Article
- Lost in Projection? Gaussian Filtering Recovers Hidden Conformational States.The journal of physical chemistry letters · 2026Article
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
- MDIntrinsicDimension: Dimensionality-Based Analysis of Collective Motions in Macromolecules from Molecular Dynamics Trajectories.Journal of chemical information and modeling · 2026Article
- Single cell transcriptomics of human weight loss links adipocyte NPY1R to control of lipolysis.Molecular metabolism · 2026Article
- Platonic representation of foundation machine learning interatomic potentials.Nature machine intelligence · 2026Article
- Sampling High-Dimensional Conformational Free Energy Landscapes of Active Pharmaceutical Ingredients.Journal of chemical theory and computation · 2025Article
- The cell as a token: high-dimensional geometry in language models and cell embeddings.Bioinformatics (Oxford, England) · 2025Review
- Uncertainty in the era of machine learning for atomistic modeling.Digital discovery · 2025Review
- Synchrony, oscillations, and phase relationships in collective neuronal activity: A highly comparative overview of methods.PLoS computational biology · 2025Article
- The intrinsic dimension of gene expression during cell differentiation.Nucleic acids research · 2025Article
- Relevant, Hidden, and Frustrated Information in High-Dimensional Analyses of Complex Dynamical Systems with Internal Noise.Journal of chemical theory and computation · 2025Article
- The topology of molecular representations and its influence on machine learning performance.Journal of cheminformatics · 2025Article
- The Intrinsic Dimension of Neural Network Ensembles.Entropy (Basel, Switzerland) · 2025Article
- miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources.Bioinformatics (Oxford, England) · 2025Article
- Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning.Scientific reports · 2025Article
- Dynamical Systems on Generalised Klein Bottles.Entropy (Basel, Switzerland) · 2025Article
- Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance.Nature communications · 2025Article
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4 authors.
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Abstract
Analyzing large volumes of high-dimensional data is an issue of fundamental importance in data science, molecular simulations and beyond. Several approaches work on the assumption that the important content of a dataset belongs to a manifold whose Intrinsic Dimension (ID) is much lower than the crude large number of coordinates. Such manifold is generally twisted and curved; in addition points on it will be non-uniformly distributed: two factors that make the identification of the ID and its exploitation really hard. Here we propose a new ID estimator using only the distance of the first and the second nearest neighbor of each point in the sample. This extreme minimality enables us to reduce the effects of curvature, of density variation, and the resulting computational cost. The ID estimator is theoretically exact in uniformly distributed datasets, and provides consistent measures in general. When used in combination with block analysis, it allows discriminating the relevant dimensions as a function of the block size. This allows estimating the ID even when the data lie on a manifold perturbed by a high-dimensional noise, a situation often encountered in real world data sets. We demonstrate the usefulness of the approach on molecular simulations and image analysis.
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