So I am trying to reduce the dimensions of my hypothetical data.

I read that PCA is a good tool but it only works for linear data set. If the data is non linear autoencoders can do a better job

First of all, how does one determine if their data is linear. Do I just plot the features against each other and see if they form a straight line?

Second, ignoring computer limitations, are autoencoders better than pca for nonlinear data.

Thanks for any comments and help!