Hi,
I have written followin the context of "Curse of Dimensionlity":

Initially the feature space is sparse but as we increase the number of variables, feature space becomes dense. Now we need more computational power for testing those features. Also with more var we have more noise added. This phenomena is called curse of dimensionality. So we have to go for reducing the dimensions which may cause loss of some features.


Is the above correct? What else can i add to it in simple words?


Zulfi.

 

How do you choose which dimensions to keep and which to eliminate?

 

How do you choose which dimensions to keep and which to eliminate?

By finding variance. We keep the dimensions which have large value of variance.

Zulfi.

 

By finding variance. We keep the dimensions which have large value of variance.

Zulfi.

So... isn't that something that you can add in simple words?

 

Yes. Good.

Thanks.

Zulfi.