Timeline for Is there any required amount of variance captured by PCA in order to do later analyses?

15 events

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Dec 5, 2020 at 0:12	comment	added	Irina		You should take into account as many Principal Components that have eigenvalues greater than 1. I think in your case the number is 4.
Feb 5, 2015 at 7:38	history	edited	doctorate	CC BY-SA 3.0	minor edit
Jan 15, 2015 at 16:36	history	edited	doctorate	CC BY-SA 3.0	deleted 12 characters in body
Jan 15, 2015 at 13:36	vote	accept	doctorate
Jan 15, 2015 at 13:07	comment	added	doctorate		@usεr11852, please see the updated caption.
Jan 15, 2015 at 12:59	history	edited	doctorate	CC BY-SA 3.0	minor edit
Jan 15, 2015 at 7:41	history	tweeted			twitter.com/#!/StackStats/status/555630752696913921
Jan 15, 2015 at 7:25	history	edited	doctorate	CC BY-SA 3.0	minor edit
Jan 15, 2015 at 0:12	answer	added	usεr11852		timeline score: 13
Jan 14, 2015 at 23:20	comment	added	usεr11852		What does the green and what do the orange/brownish lines show? There is only in axis.
Jan 14, 2015 at 20:53	history	edited	doctorate	CC BY-SA 3.0	added 11 characters in body
Jan 14, 2015 at 20:47	history	edited	doctorate	CC BY-SA 3.0	added 10 characters in body
Jan 14, 2015 at 20:40	history	edited	doctorate	CC BY-SA 3.0	added 34 characters in body
Jan 14, 2015 at 20:38	comment	added	John		The distribution of eigenvalues is pretty important for Random Matrix Theory. The Marcenko-Pastur distribution is sometimes used for similar applications.
Jan 14, 2015 at 20:32	history	asked	doctorate	CC BY-SA 3.0