Understanding principal component analysis

• 作者： Lin, Shiqiang ¹
• 作者单位：
  1.  College of Life Science, Fujian Agriculture and Forestry University
• 通讯作者： Lin, Shiqiang  Email:linshiqiang@fafu.edu.cn
• 提交时间：2023-09-15

摘要: The principal component analysis (PCA) is a frequently used machine learning method. In this paper, the PCA operation is explained by examples with Python program illustration. A proof of the diagonalizability of real symmetric matrix is also included, which may help to understand the mathematics behind PCA.

• 来自： 林世强
• 分类： 统计学 >> 应用统计数学
• 投稿状态： 未投稿
• 引用： ChinaXiv:202309.00134 (或此版本 ChinaXiv:202309.00134V1)
  doi:10.12074/202309.00134
  CSTR:32003.36.ChinaXiv.202309.00134.V1
• 推荐引用方式： Lin, Shiqiang.(2023).Understanding principal component analysis.中国科学院科技论文预发布平台.[ChinaXiv:202309.00134] (点此复制)