信息工程大学学报2025，Vol.26Issue(1)：44-50,7.DOI:10.3969/j.issn.1671-0673.2025.01.007
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
摘要
Abstract
To address the difficulty in distinguishing feature importance due to the close coupling be-tween 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 pro-posed.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 ex-tended in high-dimensional Euclidean space,and the features represented by the dimensionality aug-mentation 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Key words
convolutional neural network/feature dimensionality augmentation/weight analysis/high-dimensional Euclidean space分类
信息技术与安全科学引用本文复制引用
潘永昊,张苒苒,于洪涛,黄瑞阳,金柯君..一种针对卷积神经网络的特征升维分析方法[J].信息工程大学学报,2025,26(1):44-50,7.基金项目
嵩山实验室项目(纳入河南省重大科技专项管理体系)(221100210700-3) （纳入河南省重大科技专项管理体系）
