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[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?

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.