计算机应用研究2024，Vol.41Issue(1)：108-115,8.DOI:10.19734/j.issn.1001-3695.2023.05.0194
基于子空间学习的快速自适应局部比值和判别分析
Fast adaptive local ratio sum discriminant analysis based on subspace learning
摘要
Abstract
Dimensionality reduction is a key technique for processing high-dimensional data,and linear discriminant analysis and its variant algorithms are effective supervised algorithms.However,most discriminant analysis algorithms have the follow-ing disadvantages:a)It cannot select more discriminative features;b)It ignores the interference of noise and redundant fea-tures in the original space;c)The computational complexity of updating the adjacency graph is high.In order to overcome these shortcomings,this paper proposed a fast adaptive local ratio sum discriminant analysis algorithm based on subspace learning.Firstly,this paper proposed a model that unified the ratio sum criterion and subspace learning to explore the potential structure of the data in the subspace,select features with more discriminative information,and avoid being affected by noise in the original space.Secondly,it used an anchor-based strategies to construct an adjacency graph to represent the local structure of the data to accelerate the learning of the adjacency graph.Finally,it introduced Shannon entropy to avoid trivial solutions,and verified the effectiveness of the algorithm by comparison experiments on multiple data sets.关键词
降维/线性判别分析/子空间学习/比值和Key words
dimensionality reduction/linear discriminant analysis/subspace learning/ratio sum分类
信息技术与安全科学引用本文复制引用
曹传杰,王靖,赵伟豪,周科艺,杨晓君..基于子空间学习的快速自适应局部比值和判别分析[J].计算机应用研究,2024,41(1):108-115,8.基金项目
广东省面上自然基金资助项目(2021A1515011141) （2021A1515011141）
国防重点实验室开放基金资助项目 （）
国家自然基金青年资助项目(61904041) （61904041）
