{"title":"Research on Intelligent Application of Matrix Eigenvalue Method in Tensor Analysis","authors":"Yunkun Chen","doi":"10.1109/I-SMAC55078.2022.9987248","DOIUrl":null,"url":null,"abstract":"This paper firstly studies the intelligent application of matrix eigenvalues and eigenvalue methods based on tensor analysis. Then, starting from the concept of eigenvalues and eigenvectors and the eigenvalue decomposition theorem, through intuitive geometric demonstration, eigenvalue deployment and high-dimensional data Two specific examples of dimensionality reduction, combined with MATLAB software to clarify the geometric intuition and practical application of eigenvalues and eigenvectors, in order for students to have a deep understanding of eigenvalues and eigenvectors from multiple perspectives.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I-SMAC55078.2022.9987248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper firstly studies the intelligent application of matrix eigenvalues and eigenvalue methods based on tensor analysis. Then, starting from the concept of eigenvalues and eigenvectors and the eigenvalue decomposition theorem, through intuitive geometric demonstration, eigenvalue deployment and high-dimensional data Two specific examples of dimensionality reduction, combined with MATLAB software to clarify the geometric intuition and practical application of eigenvalues and eigenvectors, in order for students to have a deep understanding of eigenvalues and eigenvectors from multiple perspectives.