LDLE: Low Distortion Local Eigenmaps.

Dhruv Kohli, Alexander Cloninger, Gal Mishne

Abstract read

In one paragraph

Article in Journal of machine learning research : JMLR. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data.Frontiers in bioinformatics · 2022
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Dhruv KohliDepartment of Mathematics, University of California San Diego, CA 92093, USA.

Alexander CloningerDepartment of Mathematics, University of California San Diego, CA 92093, USA.

Gal MishneHalicioğlu Data Science Institute, University of California San Diego, CA 92093, USA.

Funding

Data-driven analysis for neuronal dynamic modelingR01EB026936 · NIBIB · YALE UNIVERSITY · PI MISHNE, GAL · 2018 to 2020

$1.1M

NIBIB NIH HHS R01 EB026936

6 · The paper itself

Abstract

We present Low Distortion Local Eigenmaps (LDLE), a manifold learning technique which constructs a set of low distortion local views of a data set in lower dimension and registers them to obtain a global embedding. The local views are constructed using the global eigenvectors of the graph Laplacian and are registered using Procrustes analysis. The choice of these eigenvectors may vary across the regions. In contrast to existing techniques, LDLE can embed closed and non-orientable manifolds into their intrinsic dimension by tearing them apart. It also provides gluing instruction on the boundary of the torn embedding to help identify the topology of the original manifold. Our experimental results will show that LDLE largely preserved distances up to a constant scale while other techniques produced higher distortion. We also demonstrate that LDLE produces high quality embeddings even when the data is noisy or sparse.

Indexed as

closed manifoldgraph laplacianlocal parameterizationmanifold learningnon-orientable manifoldprocrustes analysis

Identifiers

PMID35873072

PMCPMC9307127

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

None linked

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.