<p>I've been learning a little bit about computational learning theory, but most of what I've seen so far is related to supervised learning. Perhaps dimensionality reduction will be touched on, but not very in depth.</p>

<p>So I'm wondering - is there a formal notion of "generalization" in unsupervised learning that is analogous to the PAC model for supervised learning? I'm actually curious how we can get a sense of how "good" a particular unsupervised technique is without the notion of a loss function and labels.</p>

CC BY-SA 4.0
2020-04-19T23:08:06.090Z
Cross-posted: https://ai.stackexchange.com/q/20382/1794, https://cstheory.stackexchange.com/q/46660/5038.  Please [do not post the same question on multiple sites](https://meta.stackexchange.com/q/64068).
D.W.
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2020-04-19T23:08:34.827Z
@nbro, thanks for trying to help.  A request for the future - if you suggest another site, would you mind mentioning that they should delete it here before posting it elsewhere?  Hopefully that will provide a better experience for all.  Thanks for considering it.
D.W.
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2020-04-19T23:19:05.633Z
@nbro, discussing the merits of the policy is probably beyond the scope of this comment section.  If you'd like to discuss, let's take it to a separate chatroom.  You can find some of the arguments at the link I shared; and in any case, that link describes the current default Stack Exchange policy (if a site doesn't specify its own policy that overrides it), and it is policy here on this site that simultaneously cross-posting on another site is not allowed.
D.W.
CC BY-SA 4.0
2020-04-19T23:24:51.717Z
@nbro, I'd be glad to discuss it with you in a separate chatroom.
D.W.
<machine-learning><lg.learning><pac-learning>
Is there a notion of Probably Approximately Correctness in Unsupervised Learning?
Marcel
# Is there a notion of Probably Approximately Correctness in Unsupervised Learning?

(Asked by: username_0 on 2020-04-18)

I've been learning a little bit about computational learning theory, but most of what I've seen so far is related to supervised learning. Perhaps dimensionality reduction will be touched on, but not very in depth.

So I'm wondering - is there a formal notion of "generalization" in unsupervised learning that is analogous to the PAC model for supervised learning? I'm actually curious how we can get a sense of how "good" a particular unsupervised technique is without the notion of a loss function and labels.

**Tags:** machine-learning, lg.learning, pac-learning
> **Comments:**
> **username_1**: Cross-posted: https://ai.stackexchange.com/q/20382/1794, https://cstheory.stackexchange.com/q/46660/5038.  Please [do not post the same question on multiple sites](https://meta.stackexchange.com/q/64068).  
> **username_1**: @nbro, thanks for trying to help.  A request for the future - if you suggest another site, would you mind mentioning that they should delete it here before posting it elsewhere?  Hopefully that will provide a better experience for all.  Thanks for considering it.  
> **username_1**: @nbro, discussing the merits of the policy is probably beyond the scope of this comment section.  If you'd like to discuss, let's take it to a separate chatroom.  You can find some of the arguments at the link I shared; and in any case, that link describes the current default Stack Exchange policy (if a site doesn't specify its own policy that overrides it), and it is policy here on this site that simultaneously cross-posting on another site is not allowed.  
> **username_1**: @nbro, I'd be glad to discuss it with you in a separate chatroom.  

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## 0 answers

