Transfer Learning via Regularized Random-effects Linear Discriminant Analysis.

Hongzhe Zhang, Arnab Auddy, Hongzhe Li

Abstract read

In one paragraph

Article in Journal of machine learning research : JMLR, 2026. 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

3 authors.

Hongzhe ZhangDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Arnab AuddyDepartment of Statistics, The Ohio State University, Columbus, OH 43210, USA.

Hongzhe LiDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Funding

Methods for Integrative Genomic Data AnalysisR01GM129781 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI Hongzhe Lee · 2018 to 2026

$3.5M

Penn-DISAM: Data Integration and Statistical Analysis Methods for dGTEx DataU01HG013841 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI GAO, ZIYUE, LEE, HONGZHE · 2024 to 2024

$1.9M

NHGRI NIH HHS U01 HG013841NIGMS NIH HHS R01 GM129781

6 · The paper itself

Abstract

Linear discriminant analysis is a widely used method for classification. However, the high dimensionality of predictors combined with small sample sizes often results in large classification errors. To address this challenge, it is crucial to leverage data from related source models to enhance the classification performance of a target model. This paper proposes a transfer learning approach via regularized random-effects linear discriminant analysis, where the discriminant direction is estimated as a weighted combination of ridge estimates obtained from both the target and source models. Multiple strategies for determining these weights are introduced and evaluated, including one that minimizes the estimation risk of the discriminant vector and another that minimizes the classification error. Utilizing results from random matrix theory, we explicitly derive the asymptotic values of these weights and the associated classification error rates in the high-dimensional setting, where the aspect ratio

Indexed as

Covariate shiftEstimation riskHigh dimensionPrediction riskRandom matrix theoryRegularization

Identifiers

PMID42841065

PMCPMC13641122

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

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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.