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I applied PCA on MNIST data and found that the first 64 components are able to retain 86% of variance.

Is there any problem while applying pca to a big dataset like MNIST. Because in most of the papers I have read to take the components which can explain upto 99% of variance. But it would be pointless to apply pca if such variance comes at 120 pca components.

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  • $\begingroup$ Why would a reduction to a dimension of 120 be pointless? If I recall correctly, the dimension of MNIST signals is 784. $\endgroup$ – elliotp Mar 25 '18 at 7:19
  • $\begingroup$ its pca component not the dimension of mnist dataset $\endgroup$ – Boris Mar 25 '18 at 7:24

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