I'm wondering what it says about the data when, instead of dropping off dramatically the variance explained by the number of components continues increasing in a reasonably linear manner.

For example here :

enter image description here

Although the graph isn't linear, there's not a sharp decrease in the explained variance as I've seen previously.


Assuming that the x axis is number of components and the y axis is variance explained, if the relationship really was linear, it would say that your PCA is not working at all, as each PC is extracting the same amount of variance. That could happen if all your variables were orthogonal to each other.

Your graph is quite far from that. I don't think the lack of a sharp drop off says anything in particular except the more-or-less obvious point that there is no point at which further PCs suddenly become less useful.


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