GKF-PUAL: a group kernel-free approach to positive-unlabeled learning with variable selection

DOI10.1016/J.INS.2024.121574MaRDI QIDQ6645063

Jing-Hao Xue, Rui Zhu, Xiaoke Wang

Publication date: 28 November 2024

Published in: Information Sciences (Search for Journal in Brave)

zbMATH Keywords

variable selectiongroup-Lassokernel-free approachpositive-unlabeled learningtrifurcate data

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Title not available (Why is that?)
• Learning from positive and unlabeled data: a survey
• On the stopping criteria for \(k\)-nearest neighbor in positive unlabeled time series classification problems
• A biased least squares support vector machine based on Mahalanobis distance for PU learning
• Soft Quadratic Surface Support Vector Machine for Binary Classification
• The Group Lasso for Logistic Regression
• Model Selection and Estimation in Regression with Grouped Variables
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
• A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates

This page was built for publication: GKF-PUAL: a group kernel-free approach to positive-unlabeled learning with variable selection

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6645063)