How many dimensions do I need?

Question

Lisbeth Ccoyo Ortiz 2022-6-5

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1733985-how-many-dimensions-do-i-need

评论： Cholla 2023-12-26

Create a script to compute the number of feature dimensions N needed to represent at least 99.9% of the variance in the feature set of the humanactivity dataset using the 'pca' function.

The steps are:

Compute eigvals using the 'pca' function
Define vector cumulative_percent_variance_permode, which is a vector the same size as eigvals that contains 100 times (to convert fraction to percentage) the cumulative sum of the normalized eigenvalues
Define N as the number of eigenvectors needed to capture at least 99.9% of the variation in our dataset D

Script

load humanactivity.mat
D = feat; % [24075 x 60] matrix containing 60 feature measurements from 24075 samples
% compute eigvals
% compute the cumulative_percent_variance_permode vector. 
% Define N as the number of eigenvectors needed to capture at least 99.9% of the variation in D. 

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Himanshu Desai 2023-6-1

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1733985-how-many-dimensions-do-i-need#answer_1248264

在 MATLAB Online 中打开

load humact.mat
D = feat; % [24075 x 60] matrix containing 60 feature measurements from 24075 samples
% compute eigvals
[eigvects,~,eigvals] = pca(D);
% compute the cumulative_percent_variance_permode vector. 
percvar = 100*eigvals/sum(eigvals);
cumulative_percent_variance_permode = cumsum(percvar);
% Define N as the number of eigenvectors needed to capture at least 99.9% of the variation in D. 
%N = length(cumulative_percent_variance_permode (cumulative_percent_variance_permode >= 99.9))
%cumulative_percent_variance_permode
N=5;