吴恩达的机器学习编程作业18：pca pca压缩数据

最新推荐文章于 2023-12-27 17:56:35 发布

蜗牛专注学习

最新推荐文章于 2023-12-27 17:56:35 发布

阅读量527

点赞数

CC 4.0 BY-SA版权

分类专栏：机器学习 from 吴恩达

本文链接：https://blog.youkuaiyun.com/melon__/article/details/82150497

机器学习 from 吴恩达专栏收录该内容

39 篇文章

订阅专栏

该博客围绕吴恩达机器学习作业中PCA压缩数据展开，虽未给出具体内容，但可知核心是PCA在数据压缩方面的应用，这在信息技术领域的数据处理中较为重要。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

function [U, S] = pca(X)
%PCA Run principal component analysis on the dataset X
%   [U, S, X] = pca(X) computes eigenvectors of the covariance matrix of X
%   Returns the eigenvectors U, the eigenvalues (on diagonal) in S
%

% Useful values
[m, n] = size(X);

% You need to return the following variables correctly.
U = zeros(n);
S = zeros(n);

% ====================== YOUR CODE HERE ======================
% Instructions: You should first compute the covariance matrix. Then, you
%               should use the "svd" function to compute the eigenvectors
%               and eigenvalues of the covariance matrix. 
%
% Note: When computing the covariance matrix, remember to divide by m (the
%       number of examples).
%


sigma=(X'*X)/m;
[U,S,V]=svd(sigma);




% =========================================================================

end