Related question: Compressed svd algorithm implementation - #4 by odow
@Desmond it’s easier to help if you can provide a reproducible example that people can copy-and-paste.
import TestImages
import Images
import ImageInTerminal
import LinearAlgebra
function rsvd(X, k)
m, n = size(X)
Φ = rand(n, k)
Y = X * Φ
Q, R = LinearAlgebra.qr(Y)
Qm = Matrix(Q)
B = Qm' * X
Uhat, S, Vt = LinearAlgebra.svd(B)
Uk = Uhat[:, 1:k]
Vk = Vt'[1:k, :]
U = Qm * Uk
return U * LinearAlgebra.Diagonal(S) * Vk
end
image = TestImages.testimage("mandrill")
gray_image = Images.Gray.(image);
image_matrix = Images.channelview(gray_image);
M = rsvd(image_matrix, 5);
Images.colorview(Images.Gray, M)
