[Jun8]

Great article! I think you’ve done a great job of introducing these difficult concepts in simple language. I saved to Pocket and it’s already got a Best Of label there.

PCA (KLT) can be introduced as a generalization of the Fourier Transform. This can follow from using a cocktail mix analogy to Fourier Series. When I was a TA this was the approach I took with students, which seemed to make things easier for them.

An introductory post on PCA vs FA is here: https://towardsdatascience.com/what-is-the-difference-betwee...

Personal note: Susan Dumais, mentioned in the article also did great early work in text summarization, just after she joined Microsoft. I tried using some of her approached in video summarization in my PHDin early 2000s. How time flies.

[canjobear]

I don’t think explaining it as a generalization of the Fourier transform is going to help very much with New Yorker readers.

[ReaLNero]

PCA applies an orthogonal linear transformation, while FA uses a series of coefficients to scale a sequence of functions, which are then integrated. They are similar in use but very different in method. Calling one a generalization of the other seems misguided?

[aaaaaaaaaaab]

Ummm… the Fourier Transform is an orthonormal linear transformation.