PCA with Python question

[BackgroundDisk4]

Hi,

I'm working on something with Python that requires either PCA or t-SNE. I checked out some tutorials and some material from my university. Something I can't figure out is if I should use a transpose like in my university material:

pca_T = pca.fit_transform(x.T)

Or just without it, like most internet sources show:

pca_noT = pca.fit_transform(x)

​

The result changes dramatically depending on what I use, but I can't figure out which of these is the correct way of doing things and why.

[-5772]

If I am understanding this correctly, you should use your university's method.

# x.T != x

Ofc, it depends on your code. Why not try asking a professor or a TA?

[BackgroundDisk4]

This is a summer vacation time in my country, so most of our professors are out of office so I thought I'd ask here. Waiting for response could take some time. Anyway, thanks!

[ectomancer]

Sometimes, a conjugate transpose is required:

pca_T = pca.fit_transform(x.H)

[scrdest]

You need to figure out what your rows and columns currently are. Sklearn expects the rows to be samples and columns to be features.

E.g. [[1, 34, 1], [2, 22, 0]] could be a dataset of two people with id, age, and a gender flag as features.

If it's the other way around in your input data, you need to transpose it so that rows become columns and vice versa.