ArticleIEEE access : practical innovations, open solutions2018
An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning.
Article in IEEE access : practical innovations, open solutions, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
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Who cites it
7 citing papers in PubMed.
- From disorganized data to emergent dynamic models: Questionnaires to partial differential equations.PNAS nexus · 2025Article
- On learning what to learn: Heterogeneous observations of dynamics and establishing possibly causal relations among them.PNAS nexus · 2024Article
- Limits of entrainment of circadian neuronal networks.Chaos (Woodbury, N.Y.) · 2023Article
- Learning emergent partial differential equations in a learned emergent space.Nature communications · 2022Article
- Local conformal autoencoder for standardized data coordinates.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Emergent Spaces for Coupled Oscillators.Frontiers in computational neuroscience · 2020Article
- Linking Gaussian process regression with data-driven manifold embeddings for nonlinear data fusion.Interface focus · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here on the case of time series data that can ultimately be modelled as a spatially distributed system (e.g. a partial differential equation, PDE), but where we do not know the space in which this PDE should be formulated. Hence, even the spatial coordinates for the distributed system themselves need to be identified - to "emerge from"-the data mining process. We will first validate this "emergent space" reconstruction for time series sampled
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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.