How many dimensions do I need?

Question

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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. 

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Answer 1

Himanshu Desai 2023-6-1

在 MATLAB Online 中打开

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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;