[D] What is the effect of training a system with jumbled up feature vectors?
An idea has been bouncing around my head today, and since I’m not very knowledgeable about ML I want to know if there’s any literature (or common sense from experts) about it.
Lets say we have training examples consisting of (say) two features, color (c
) and size (s
), such that v1 = [c1, s1]
, v2 = [c2, s2]
, and vn = [cn, sn]
.
What is the effect of training a system with “jumbled” inputs vx
, and vy
, such that vx = [c1, s2]
and vy = [c2, s1]
?
My immediate thought is that you can’t really give labels to jumbled training examples (it can’t be a cat if it has a horse’s head and a pig’s tail), but perhaps the system could learn a probability distribution of the labels based on the features included?
Anyway, can jumbled training examples produce a model that is useful in any way? Is there any literature exploring this?
submitted by /u/General_Example
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