南京师大学报（自然科学版）2025，Vol.48Issue(4)：96-105,10.DOI:10.3969/j.issn.1001-4616.2025.04.010
基于局部邻接图的半监督稀疏回归算法
Semi-Supervised Sparse Regression Based on Local Adjacency Graph
摘要
Abstract
Semi-supervised regression(SSR)algorithms leverage a small amount of labeled samples along with a large pool of unlabeled samples for modeling regression functions,which can alleviate the high costs associated with obtaining labeled data to some extent.At present,graph-based SSR algorithms have been proposed,which can employ adjacency matrices to dig the underlying data structure.However,existing graph-based SSR methods are confronted with two primary challenges.First,existing methods rely on the fully-connected graph on data that is susceptible to noise and outliers.Second,the sparsity of current SSR algorithms leaves room for enhancement.To address these issues,this paper proposes a novel semi-supervised sparse regression algorithm.This algorithm designs a new graph,the local adjacency graph,which focuses on the local connectivity of samples.This graph generation method preserves the local manifold structure of the data and mitigates the impact of noisy samples.Furthermore,our algorithm capitalizes on the sparsity-inducing property of the 1-norm regularization by incorporating a 1-norm regularization term for the model coefficients into the optimization problem,effectively enhancing the model's sparsity.Empirical validation is performed on nine real-world datasets to evaluate the SSR performance and the sparsity of the proposed algorithm.Results demonstrate that our method achieves superior performance across various experimental setups.关键词
半监督回归学习/图生成/邻接矩阵/稀疏性/1范数正则Key words
semi-supervised regression learning/adjacency matrix/graph generation/sparsity/1-norm regularization分类
信息技术与安全科学引用本文复制引用
秦晓燕,郑晓晗,张莉..基于局部邻接图的半监督稀疏回归算法[J].南京师大学报（自然科学版）,2025,48(4):96-105,10.基金项目
江苏省高校自然科学研究资助项目(19KJA550002)、江苏省六大人才高峰资助项目(XYDXX-054)、江苏省职业教育软件技术"双师型"名师工作室资助项目(苏教师函[2022]31号). （19KJA550002）
