Hello,

As per my understanding PCA and SVD used for Dimensionality reduction by removing the redundant/correlated variables then What is the difference between SVD and PCA ?

Can somebody explain ?

Thanks,

Sirisha.

Hello,

As per my understanding PCA and SVD used for Dimensionality reduction by removing the redundant/correlated variables then What is the difference between SVD and PCA ?

Can somebody explain ?

Thanks,

Sirisha.

Hello,

You can get more details here:

And in the additional references included.

Regards,

Carlos Ortega

Hi,

Although the post is quite old, I came across this recently and would like to share my understanding on the topic.

PCA (principal component analysis) is a method of extracting important variables (in form of components) from a large set of variables available in a data set. The idea is to calculate and rank the importance of features/dimensions.

In order to do that, we use SVD (Singular value decomposition). SVD is used on covariance matrix to calculate and rank the importance of the features.

You can refer the below mentioned discussion thread for clarification on PCA and covariance matrix:

Also, the below article provides codes in R and python for PCA implementation.