Finding intrinsic dimensionality of data set

3 次查看（过去 30 天）
Desiree
Desiree 2020-1-24
回答： Gaurav Garg 2020-2-3
Suppose I have a random (100,10) matrix. Here’s a code that gives the pca:
rng 'default'
X=rand(100,10);
X=bsxfun(@minus,X,mean(X));
[coeff,score,latent]=pca(X);
covmatrix=cov(X);
[V,D]=eig(covmatrix);
coeff
V
dataprincipalspace=X*coeff;
score
corrcoef(dataprincipalspace);
var(dataprincipalspace)'
latent
sort(diag(D),'descend')
If now I wish to know the intrinsic dimension of it, what should I add to my code? Help is appreciated!

请先登录，再回答此问题。

采纳的回答

Gaurav Garg
Gaurav Garg 2020-2-3
Hi,
latent (column vector) stores the eigenvalues of the covariance matrix of X.
Executing
cumsum(latent/sum(latent))
would tell you the % of data variance in each dimension.
Finally, the number of dimensions will depend on how much variance you wish to have in your data.
For example, in your case it comes out to be ~ 94% of variance upto 9th dimension.

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Dimensionality Reduction and Feature Extraction 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by