Principal Component Analysis (PCA) for Images and Signals

[@liketheblue5082]

This is really the best explaination I have seen so far. You deserve much more likes. Thank you!

[@moreiralucascde]

Great and very clear explanation, thank you very much sir for sharing your knowledge.

[@michelleelizabeth9956]

Thanks for clear explanation 👍

[@MonaAwad123]

Thank you so much , I need the source papers pls

[@exploringtechnologies9]

This u can find from any standard book.

[@hoaithuong4780]

i'm doing classify 4 kinds of animals, and using PCA to visualize 2D of data at the first step. And I'm turning all images into grayscale because it's much less complicated. Can you please tell me that if I do like that, i will miss many feature at the first RGB image ? and what should I do, thank you so much

[@exploringtechnologies9]

Grayscale images will work fine. No problem u will face.

[@ErdemMERCAN-rv7yn]

Hi, I have 2D images which I am currenty working on. their size 64x64. Is this a common approach to turning them 1x4096(kind of a 1D spectra). I have tried this method and it seems working for my data set. However can I justify this method if I am going to use it for my publications.

[@exploringtechnologies9]

Don't call it as spectra.
It is just reshape of matrix from 2D to 1D.
Yes, u can go for publication.

[@Umarfaroq531]

Sir pdf form ma send krdai pz

[@exploringtechnologies9]

It's copyrighted material.