一种针对卷积神经网络的特征升维分析方法  

A Feature Dimensionality Augmentation Analysis Method for CNN

作　　者：潘永昊 张苒苒 于洪涛 黄瑞阳[1] 金柯君 PAN Yonghao;ZHANG Ranran;YU Hongtao;HUANG Ruiyang;JIN Kejun(Information Engineering University,Zhengzhou 450001,China;Unit 31015,Beijing 100840,China;Unit 66389,Handan 056000,China)

机构地区：[1]信息工程大学,河南郑州450001 [2]31015部队,北京100840 [3]66389部队,河北邯郸056000

出　　处：《信息工程大学学报》2025年第1期44-50,共7页Journal of Information Engineering University

基　　金：嵩山实验室项目(纳入河南省重大科技专项管理体系)(221100210700-3)。

摘　　要：针对卷积神经网络模型与输入数据紧密耦合的特性导致特征重要性难以区分的问题,提出一种从网络模型的输出结果中分析输入特征重要性的特征升维分析方法。首先,在高维欧氏空间中对输入网络模型的样本特征依次分配一个标准正交基,对输入样本特征进行升维表示;其次,在高维欧氏空间中对卷积神经网络进行计算扩展,并对升维表示的特征进行计算;最后,在计算结果中可由标准正交基与输入样本特征的对应关系,分析得出各个输入样本特征在输出结果中的影响权值。实验表明该方法分析得出的权重能够有效反映输入特征对卷积神经网络的影响力。To address the difficulty in distinguishing feature importance due to the close coupling between convolutional neural networks models and input data,a feature dimensionality augmentation method to analyze the importance of input features from the output results of the network model is proposed.Firstly,a standard orthogonal basis is sequentially assigned to the sample features of the input network model in a high-dimensional Euclidean space,and the input sample features are represented with dimensionality augmentation.Secondly,the convolutional neural networks is computationally extended in high-dimensional Euclidean space,and the features represented by the dimensionality augmentation are computed.Finally,in the calculation results,the corresponding relationship between the standard orthogonal basis and the input sample features can be analyzed to determine the influence weights of each input sample feature in the output results.Experiment shows that the weights analyzed in this method can effectively reflect the influence of input features on convolutional neural networks.

关 键 词：卷积神经网络 特征升维 权重分析 高维欧式空间 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]