SVD

In Linear Algebra Course, SVD is one of matrix decomposition methods. (such as LU, QR)

It decomposes a original matrix into 3 matrices.

X =~ U * Sigma * V(transpose)

Using ranks for appx

Represent original matrix using handful of cols and rows.

Smaller the number of ranks, smaller the image size maintaining the featrues of the image.

• YOUTUBE VIDEO LINK: https://www.youtube.com/watch?v=H7qMMudo3e8

• dark programmer: https://darkpgmr.tistory.com/106