# memory pca vs pcacov

조회 수: 9(최근 30일)
Dear all,
I'm running a pca on a large matrix (33*500,000) and with pcacov I get a memory error, but pca gives me no trouble. Could anyone explain this to me? Is the matrix somehow being reduced before computing the covariance matrix in pca?
Thanks!
Best
Hans

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### 답변(3개)

Hiro 2 Nov 2020
In PCA, your matrix (p x q) will be once converted into the variance-covariance matrix (q x q).
This would reqiure huge memory comsumption. Meanwhile, pcacov accepts a variance-covariance matrix as an input and, therefore the argument (input) should be a square matrix though.
As for big data anaysis, you may want to use tall array - this can be a solution.
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Hans van der Horn 2 Nov 2020
Dear Hiro,
Thanks for your answer. If I compute cov(my matrix) to get the input for pcacov I run out of memory. However, if I run the pca function on my matrix. Why does the pca.m not get into memory problems?
Best
Hans
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Hiro 2 Nov 2020
I do not believe pcacov works with your matrix in the first place since the shape of your matrix is unacceptable. I do not know what the error will be like.

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Hans van der Horn 2 Nov 2020
Dear Hiro,
The problem is that by constructing the covariance matrix Matlab runs out of memory. Why does the pca.m function not give memory problems (when I of course enter the original matrix)?
thanks!
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Hans van der Horn 2 Nov 2020
thanks! It's clear to me now:) I will also read the documentation for PCA (I thought pcacov also used svd..).

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