摘要
为了解决字典学习中的高维数据与非线性问题,提出一种基于降维字典学习的高维数据分类策略。在降维阶段,利用自编码器学习一种非线性映射,该映射可以降维并保留高维数据的非线性结构;在字典学习阶段,利用标签嵌入进行局部约束;在学习过程中,保留了可分解的非线性局部结构,增强了类的区分能力,同时优化了映射函数和字典。在多个基准数据集上的实验结果表明,提出的方法能够有效解决字典学习中的高维数据与非线性问题。
In order to solve the problem of high-dimensional data and non-linearity in dictionary learning,a high dimensional data classification strategy based on reduced dimension dictionary learning is proposed.In the dimension reduction stage,the automatic encoder was used to learn a nonlinear mapping,which could reduce the dimension and retain the nonlinear structure of high-dimensional data.In the dictionary learning stage,label embedding was used for local constraints.In the learning process,the decomposable nonlinear local structure was retained,the ability to distinguish classes was enhanced,and the mapping function and dictionary were optimized.Experimental results on several benchmark data sets show that the proposed method can effectively solve the problems of high-dimensional data and nonlinearity in dictionary learning.
作者
李巧君
李江岱
王爱菊
Li Qiaojun;Li Jiangdai;Wang Aiju(School of Electronic and Information Engineering,Henan Polytechnic Institute,Nanyang 473000,Henan,China;College of Information Engineering,Zhengzhou University of Technology,Zhengzhou 450000,Henan,China)
出处
《计算机应用与软件》
北大核心
2024年第9期329-338,共10页
Computer Applications and Software
基金
河南省科技攻关项目(212102310086,212102210398)
河南省高等职业学校青年骨干教师培养计划(教职成函〔2019〕326号)。
关键词
字典学习
高维数据
局部约束
自编码器
Dictionary learning
High dimensional data
Local constraints
Automatic encoder
