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Testing the manifold hypothesis
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The hypothesis that high dimensional data tend to lie in the vicinity of a low dimensional manifold is the basis of manifold learning. The goal of this paper is to develop an algorithm (with accompanying complexity guarantees) for testing the existence of a manifold that fits a probability distribution supported in a separable Hilbert space, only using i.i.d. samples from that distribution. More precisely, our setting is the following. Suppose that data are drawn independently at random from a probability distribution P supported on the unit ball of a separable Hilbert space H . Let G ( d , V , τ ) be the set of submanifolds of the unit ball of H whose volume is at most V and reach (which is the supremum of all r such that any point at a distance less than r has a unique nearest point on the manifold) is at least τ . Let L ( M , P ) denote the mean-squared distance of a random point from the probability distribution P to M . We obtain an algorithm that tests the manifold hypothesis in the following sense. The algorithm takes i.i.d. random samples from P as input and determines which of the following two is true (at least one must be): There exists M ∈ G ( d , C V , τ C ) such that L ( M , P ) ≤ C ϵ . There exists no M ∈ G ( d , V / C , C τ ) such that L ( M , P ) ≤ ϵ C . The answer is correct with probability at least 1 − δ .
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“According to the manifold hypothesis [66], most natural data lies on manifolds with significantly reduced intrinsic dimensionality.”
From Diffusion Models: A Comprehensive Survey of Methods and Applications (2024) · cites this paper as [66]
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“Data with manifold structures are ubiquitous in machine learning.As the manifold hypothesis [66] posits, natural data often reside on manifolds with lower intrinsic dimensionality.”
From Diffusion Models: A Comprehensive Survey of Methods and Applications (2024) · cites this paper as [66]
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Self-citation
Metadata
- Authors
- Fefferman, C., Mitter, S., & Narayanan, H.
- Affiliations
- Princeton University (US)
- Massachusetts Institute of Technology (US)
- University of Washington (US)
- Venue
- Journal of the American Mathematical Society
- Publisher
- American Mathematical Society (AMS)
- Published
- 2016-02-09
- Volume
- 29
- Issue
- 4
- Pages
- 983-1049
- ISSN
- 0894-0347 (ISSN-L)
- 1088-6834
- OpenAlex
- W1593775104
- Cited by
- 430
- References
- 11
- Citations by year
- 2026: 50 · 2025: 72 · 2024: 57 · 2023: 63 · 2022: 49 · 2021: 62 · 2020: 34 · 2019: 29 · 2018: 8 · 2017: 3
- FWCI
- 4.20
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Mathematical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (EECS -1135843 " Smart Power Systems of the Future : Foundations for Understanding Volatility and Improving Operational Reliabili)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Mathematics › Applied Mathematics › Geometric Analysis and Curvature Flows
- Keywords
- manifold hypothesis, manifold learning, separable Hilbert space, i.i.d. samples, high-dimensional data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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