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Sampling matrix #4

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Weixin-An opened this issue Jan 2, 2022 · 6 comments
Open

Sampling matrix #4

Weixin-An opened this issue Jan 2, 2022 · 6 comments

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@Weixin-An
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How is the sampling matrix obtained? which algorithm is used? Thanks a lot!

@usanzhu
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usanzhu commented Dec 11, 2023

hi,have you sovled this problem?I’m confused with it, too. If solved, could you please help me with it, thanks!!

@Guaishou74851
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The following code provides a method of generating an M-by-N random Gaussian matrix:

import numpy as np
from scipy.linalg import orth

M = 512
N = 1024
Phi = orth(np.random.randn(N, N))[:M]

print(Phi @ Phi.T)

@usanzhu
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usanzhu commented Dec 12, 2023

The following code provides a method of generating an M-by-N random Gaussian matrix:

import numpy as np
from scipy.linalg import orth

M = 512
N = 1024
Phi = orth(np.random.randn(N, N))[:M]

print(Phi @ Phi.T)

thanks a lot!!

@huyanjia
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以下代码提供了一种生成 M×N 随机高斯矩阵的方法:

你好,采用此代码随机生成的高斯矩阵需要正交化或者其他变化吗,这样训练之后重建的效果好吗

@huyanjia
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The following code provides a method of generating an M-by-N random Gaussian matrix:

import numpy as np
from scipy.linalg import orth

M = 512
N = 1024
Phi = orth(np.random.randn(N, N))[:M]

print(Phi @ Phi.T)

Hello! Do the randomly generated Gaussian matrices from this code need to be orthogonalized or otherwise modified? Will the reconstruction quality be good after training?

1 similar comment
@huyanjia
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The following code provides a method of generating an M-by-N random Gaussian matrix:

import numpy as np
from scipy.linalg import orth

M = 512
N = 1024
Phi = orth(np.random.randn(N, N))[:M]

print(Phi @ Phi.T)

Hello! Do the randomly generated Gaussian matrices from this code need to be orthogonalized or otherwise modified? Will the reconstruction quality be good after training?

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4 participants