Journal of Shanghai Jiao Tong University ›› 2021, Vol. 55 ›› Issue (10): 1320-1329.doi: 10.16183/j.cnki.jsjtu.2020.276

Special Issue: 《上海交通大学学报》2021年12期专题汇总专辑 《上海交通大学学报》2021年“自动化技术、计算机技术”专题

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Orthogonal Features Extraction Method and Its Application in Convolution Neural Network

LI Chen, LI Jianxun()   

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2020-09-08 Online:2021-10-28 Published:2021-11-01
  • Contact: LI Jianxun


In view of feature redundancy in the convolutional neural network, the concept of orthogonal vectors is introduced into features. Then, a method for orthogonal features extraction of convolutional neural network is proposed from the perspective of enhancing the differences between features. By building the structure of parallel branches and designing the orthogonal loss function, the convolution kernels can extract the orthogonal features, enrich the feature diversity, eliminate the feature redundancy, and improve the results of classification. The experiment results on one-dimensional sample dataset show that compared with the traditional convolution neural network, the proposed method can supervise the convolution kernels with different sizes to mine more comprehensive information of orthogonal features, which improves the efficiency of convolutional neural network and lays the foundation for subsequent researches on pattern recognition and compact neural network.

Key words: convolution neural network, feature redundancy, orthogonality, feature vector

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