Article... International Conference on Sampling Theory and Applications (SampTA). International Conference on Sampling Theory and Applications2023
On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical Phenomena.
Article in ... International Conference on Sampling Theory and Applications (SampTA). International Conference on Sampling Theory and Applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- MANIFOLD LEARNING IN METRIC SPACES.Applied and computational harmonic analysis · 2026Article
- Amortized template matching of molecular conformations from cryoelectron microscopy images using simulation-based inference.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cryo-EM Heterogeneity Analysis using Regularized Covariance Estimation and Kernel Regression.bioRxiv : the preprint server for biology · 2024Article
- Methods for Cryo-EM Single Particle Reconstruction of Macromolecules Having Continuous Heterogeneity.Journal of molecular biology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Many techniques in machine learning attempt explicitly or implicitly to infer a low-dimensional manifold structure of an underlying physical phenomenon from measurements without an explicit model of the phenomenon or the measurement apparatus. This paper presents a cautionary tale regarding the discrepancy between the geometry of measurements and the geometry of the underlying phenomenon in a benign setting. The deformation in the metric illustrated in this paper is mathematically straightforward and unavoidable in the general case, and it is only one of several similar effects. While this is not always problematic, we provide an example of an arguably standard and harmless data processing procedure where this effect leads to an incorrect answer to a seemingly simple question. Although we focus on manifold learning, these issues apply broadly to dimensionality reduction and unsupervised learning.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.