Dealing with missing data

[LarsOL]

I am contemplating using LSH in my application, but I am unsure how to deal with absent/missing data in a vector. The nearest neighbor imputation implies that this type of algorithm deals with this scenario, but how would I go about implementing it?

[tdebatty]

Hi! I never had to test this, but my guess would be providing default values... Le ven. 16 déc. 2016 02:34, Lars Lawoko <notifications@github.com> a écrit :

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I am contemplating using LSH in my application, but I am unsure how to deal with absent/missing data in a vector. The nearest neighbor imputation implies that this type of algorithm deals with this scenario, but how would I go about implementing it? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#12>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AA1SDJ8n5yhHkpojA044HXTz_gwhv3SCks5rIeqjgaJpZM4LOwp3> .

[LarsOL]

The main issue I see with providing a default value is that; wouldn't the values be artificially clustered around those "default" values that seem valid for the algorithm ? Random data may work, but then it is not deterministic.

Ideally what would happen is you can ignore a dimension if there is not a value in it.