Titre
Statistical Quantile Learning for Large, Nonlinear, and Additive Latent Variable Models.
Type
autre
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Auteur(s)
Bodelet, Julien
Auteure/Auteur
Blanc, Guillaume
Auteure/Auteur
Shan, Jiajun
Auteure/Auteur
Terrera, Graciela M.
Auteure/Auteur
Chén, Oliver Y
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
Date de publication
2023-12-29
Langue
anglais
Résumé
The studies of large-scale, high-dimensional data in fields such as genomics and neuroscience have injected new insights into science. Yet, despite advances, they are confronting several chal- lenges, often simultaneously: lack of interpretability, nonlinearity, slow computation, inconsistency and uncertain convergence, and small sample sizes compared to high feature dimensions. Here, we propose a relatively simple, scalable, and consistent nonlinear dimension reduction method that can potentially address these issues in unsupervised settings. We call this method Statistical Quantile Learning (SQL) because, methodologically, it leverages on a quantile approximation of the latent variables together with standard nonparametric techniques (sieve or penalyzed methods). We show that estimating the model simplifies into a convex assignment matching problem; we derive its asymptotic properties; we show that the model is identifiable under few conditions. Compared to its linear competitors, SQL explains more variance, yields better separation and explanation, and delivers more accurate outcome prediction. Compared to its nonlinear competitors, SQL shows considerable advantage in interpretability, ease of use and computations in large-dimensional set- tings. Finally, we apply SQL to high-dimensional gene expression data (consisting of 20, 263 genes from 801 subjects), where the proposed method identified latent factors predictive of five cancer types. The SQL package is available at https://github.com/jbodelet/SQL.
PID Serval
serval:BIB_0622E4AB21A0
Date de création
2024-01-11T17:05:38.584Z
Date de création dans IRIS
2025-05-20T13:23:45Z
Fichier(s)![Vignette d'image]()
En cours de chargement...
Nom
SQL.pdf
Version du manuscrit
preprint
Taille
2.91 MB
Format
Adobe PDF
PID Serval
serval:BIB_0622E4AB21A0.P001
URN
urn:nbn:ch:serval-BIB_0622E4AB21A04
Somme de contrôle
(MD5):aad417aab9bac6489ba57770c89e5e22