Rootlets Hierarchical Principal Component Analysis for Revealing Nested Dependencies in Hierarchical Data.

Abstract read

In one paragraph

Article in Mathematics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Korey P WylieDepartment of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Anschutz Health Sciences Building, 1890 N Revere Ct, Aurora, CO 80045, USA.ORCID 0000-0003-1331-9611

Jason R TregellasDepartment of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Anschutz Health Sciences Building, 1890 N Revere Ct, Aurora, CO 80045, USA.

Funding

Neuronal and behavioral effects of an implicit priming approach to improve eating behaviors in obesity: Diversity supplement.R01DK125417 · NIDDK · UNIVERSITY OF COLORADO DENVER · PI TREGELLAS, JASON R · 2021 to 2025

$2.7M

CSRD VA IK6 CX002178NIDDK NIH HHS R01 DK125417

6 · The paper itself

Abstract

Hierarchical clustering analysis (HCA) is a widely used unsupervised learning method. Limitations of HCA, however, include imposing an artificial hierarchy onto non-hierarchical data and fixed two-way mergers at every level. To address this, the current work describes a novel rootlets hierarchical principal component analysis (hPCA). This method extends typical hPCA using multivariate statistics to construct adaptive multiway mergers and Riemannian geometry to visualize nested dependencies. The rootlets hPCA algorithm and its projection onto the Poincaré disk are presented as examples of this extended framework. The algorithm constructs high-dimensional mergers using a single parameter, interpreted as a

Indexed as

eigendecompositionhyperbolic manifoldmanifold learningmultivariate statisticsRiemannian geometry

Identifiers

PMID40995068

DOI10.3390/math13010072

PMCPMC12456745

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.