PCA 6 - Relationship to SVD

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Errata:
1:35 - Both the rows and columns of U are actually orthonormal.

Herman Kamper

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This fills in so many gaps I had in my knowledge on PCA and SVD. Thank you

shuddR

One of the best explanations on PCA relationship with SVD!

ozysjahputera

Thank you very much for this video. I spend many time in stackexchange trying to understand this relationship, but you explain it really well in a short video. Thank you so much.

RaksohOap

the best explanation of pca and how it relates to svd and the eigen value and eigen vectors 🙌

RaviChoudhary_iitkgp

Excellent video..always had some doubt on why we take the covariance matrix when computing eigen decomposition. Thank you and had a lot of my doubts clarified by this video.

mvijayvenkatesh

Brilliant, You forgot to center the data but that's a trivial oversight.

Trubripes

Excellent explanation. I could join all the dots regarding my understanding! Thanks a lot :)

atrayeedasgupta

the best explanation I've heard on this subject!

johnjunhyukjung

Nice video ! Note that X has to be centered (I don't know the definition of design matrix though x) ) so that we have the a correct covariance matrix

zenchiassassin

in 8:00 you say that "we can get all the eigen vector and eigen values by just doing the SVD of X' * X (sample coveriance matrix)" but I think what you mean is by just simply doing the eigen decomposition of X' * X, or ultimately SVD of X' and take the V values, am I correct?

xerocool

a great video. I have learned a lot from your machine learning series. 😁

m.preacher

Oh man, your channel its amazing, i am from brazil and i liked of your explanation about this content.. congratulations.

librarymy-nb

Really nice explanation, thanks for this! :)

janlukasr.

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