Two column
Left
We have a number of n-dimensional points; Normally, we identify each one of them using n numbers (e.g., the point (0,1) in R^2 is identified using 2 numbers)
Right
But, what if we could give n-dimensional points a lower dimensional (<n) identifier?
This only make sense if...
This only make sense if the n-dim points are mostly within a finite subset of their n-dim space, which allows them to be mapped onto a lower (<n)-dim space.
We could use a hash map, and it’d be perfectly able to map an n-dim point to a (<n)-dim point, but would it be able to map the (<n)-dim point back to its n-dim version? Nope.
An Autoencoder, however, can map back and forth between an n-dim point and a (<n)-dim one.
It in fact can do all of the following stuff that a hash map just can’t,
- …
- map an n-dim point, A, to a (<n)-dim that then gets mapped back into another n-dim point, B.
- This is useful if u want information in A that’s not in B (usually noise) to be ignored in the mapping-back.
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Example
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Related Notes
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