Evidence map›Paper›PMID 41323121›Full record

ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2025

Dynamic Supervised Principal Component Analysis for Classification.

Wenbo Ouyang, Ruiyang Wu, Ning Hao, Hao Helen Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2025. 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.

  1. Dynamic Supervised Principal Component Analysis for Classification.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025
    Article
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

4 authors.

Wenbo OuyangGIDP in Statistics and Data Science, University of Arizona.
Ruiyang WuPaul H. Chook Department of Information Systems and Statistics, Baruch College, The City University of New York.
Ning HaoGIDP in Statistics and Data Science, University of Arizona.
Hao Helen ZhangGIDP in Statistics and Data Science, University of Arizona.

Funding

Ovarian Cancer Detection with Blood- and Imaging-Based BiomarkersR01CA260399 · NCI · UNIVERSITY OF ARIZONA · PI Jennifer Kehlet Barton, David A. Fishman · 2022 to 2026
$3.9M
Multiclass classification under prioritized error control and specific error costs with applications to dementia classificationR21AG074205 · NIA · NEW YORK UNIVERSITY · PI FENG, YANG · 2021 to 2022
$438k
NCI NIH HHS R01 CA260399NIA NIH HHS R21 AG074205
6 · The paper itself

Abstract

This paper introduces a novel framework for dynamic classification in high dimensional spaces, addressing the evolving nature of class distributions over time or other index variables. Traditional discriminant analysis techniques are adapted to learn dynamic decision rules with respect to the index variable. In particular, we propose and study a new supervised dimension reduction method employing kernel smoothing to identify the optimal subspace, and provide a comprehensive examination of this approach for both linear discriminant analysis and quadratic discriminant analysis. We illustrate the effectiveness of the proposed methods through numerical simulations and real data examples. The results show considerable improvements in classification accuracy and computational efficiency. This work contributes to the field by offering a robust and adaptive solution to the challenges of scalability and non-staticity in high-dimensional data classification.

Indexed as

Dimension reductionDiscriminant analysisGene expression dataHigh-dimensional dataKernel smoothing

Identifiers

PMID41323121
PMCPMC12662483

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.