I am studying about dimension reduction and while studying about I came across two methods

1-factor analysis

2-PCA

while studying it I learn that they are almost same but I am not able understand what is exact difference between them

# What is difference between factor analysis and principal component analysis

**hinduja1234**#1

If Your main aim is only to reduce observed data - use PCA. If Your

aim is to reason about latent factors - use factor analysis.

This short tutorial will give you a very good understanding of factor analysis:

On the other hand, principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Hope this helps!