电子科技2026，Vol.39Issue(6)：1-11,11.DOI:10.16180/j.cnki.issn1007-7820.2026.06.001
基于张量CP分解的不完整数据自适应图特征选择
Adaptive Graph Feature Selection Based on Tensor CP Decomposition for Incomplete Data
摘要
Abstract
The feature selection problem for tensor data is a complex task that requires comprehensive consider-ation of the multi-dimensional structure information of the data and incomplete data.To solve this problem,an adap-tive graph regularization feature selection algorithm for incomplete data based on tensor CP(Canonical Polyadic)de-composition is proposed.The collaborative design of data filling and feature selection reduces the high computational load of the traditional staged processing method,effectively improving the learning efficiency.The tensor CP decom-position is utilized to precisely fill the incompleteness of the data.The introduction of an adaptive graph structure based on the Gini coefficient can more accurately embed the geometric structure of the data into manifold learning,which not only effectively enhances the robustness and computational efficiency of the model,but also obtains the op-timal feature subset of the data.Comparison results of six feature selection algorithms on six public datasets verifies the effectiveness of the proposed algorithm.关键词
特征选择/过滤式方法/非负CP分解/不完整张量数据/图拉普拉斯正则化/基尼系数/优化算法/线性分类器Key words
feature selection/filtering method/non-negative CP decomposition/incomplete tensor data/graph Laplacian regularization/Gini coefficient/optimization algorithm/linear classifier分类
信息技术与安全科学引用本文复制引用
刘家徐,宋燕,窦军,张亚萌..基于张量CP分解的不完整数据自适应图特征选择[J].电子科技,2026,39(6):1-11,11.基金项目
国家自然科学基金(62073223) （62073223）
上海市自然科学基金(22ZR1443400)National Natural Science Foundation of China(62073223) （22ZR1443400）
Natural Science Foundation of Shanghai(22ZR1443400) （22ZR1443400）
