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Timeline for What is "feature space"?

Current License: CC BY-SA 3.0

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Nov 3, 2019 at 5:06 comment added Quinn Culver @HasanIqbal What do you think about the probability distribution on $\mathbb{R}^4$ induced by function $(x_1, x_2, x_3) \mapsto (x_1, x_2,x_3, x_1 / x_2)$? Could that be considered effectively the same as enlarging the sample space?
Aug 7, 2019 at 12:53 comment added JJJohn @HasanIqbal What does "It's exhaustive" mean? Take the iris dataset as example, there are 150 instances and 4 attributes? Would you please illustrate "It's exhaustive, feature spaces aren't"?
Aug 6, 2019 at 15:46 comment added ABIM Ahaha, in that case great post x2 :)
Aug 6, 2019 at 15:44 comment added Cam.Davidson.Pilon @AIM_BLB that's me!
Aug 6, 2019 at 15:22 comment added ABIM @Cam.Davidson.Pilon someone wsa inspired by your answer it seems: dataorigami.net/blogs/napkin-folding/…
Apr 5, 2016 at 12:56 comment added QuestionEverything I would say that, as Pilon's example shows, feature space can be increased by extracting some new features. Sample space in probability can't. It's exhaustive, feature spaces aren't.
Mar 13, 2015 at 15:39 review Suggested edits
Mar 13, 2015 at 16:32
S Dec 24, 2012 at 4:22 history suggested soufanom CC BY-SA 3.0
Replacing "creation" eith "extraction". More accurate term from literature.
Dec 24, 2012 at 4:07 review Suggested edits
S Dec 24, 2012 at 4:22
Dec 23, 2012 at 4:25 comment added Cam.Davidson.Pilon It's is very similar, if not identical. If you consider the data-generating distribution $D$, then the feature-space is identical to the support of $D$.
Dec 23, 2012 at 4:24 vote accept power
Dec 23, 2012 at 2:43 comment added Placidia How does this differ from a sample space in probability theory? Just asking. I would like to know.
Dec 22, 2012 at 17:11 history edited Cam.Davidson.Pilon CC BY-SA 3.0
added 51 characters in body
Dec 22, 2012 at 17:05 history answered Cam.Davidson.Pilon CC BY-SA 3.0