You can also reduce the dimensionality using random projections.
Arnaud
On 28 Jan 2014, at 11:39, Nick Pentreath <nick.pentre...@gmail.com> wrote:
> Another important and related use case is to reduce the search space, for
> example, in recommendation systems one often has to do the dot product, or
> cosine similarity, between two vectors of moderate dimension. But you have to
> do this in real-time across potentially millions of candidate items. In this
> case the search space can be reduced to those candidate vectors that are
> estimated to be "nearest" to the user ("query") vector.
>
>
> On Tue, Jan 28, 2014 at 12:28 PM, Robert Layton <robertlay...@gmail.com>
> wrote:
> Yes, but it doesn't suffer so much at high dimensions, as compared to
> something like the Euclidean distance.
>
>
> On 28 January 2014 20:59, Joel Nothman <joel.noth...@gmail.com> wrote:
> I have previously seen that there is interest in LSH in scikit-learn, but
> don't know much about its application to machine learning. Is it basically
> used for nearest neighbour methods?
>
>
> On 28 January 2014 20:48, Robert Layton <robertlay...@gmail.com> wrote:
> In principle, I'm happy to be a mentor for LSH, as I've used it quite a bit
> and implemented nilsimsa in python and javascript, as well as tested a number
> of other algorithms.
> I don't know much about GSOC though. What would I need to do?
>
>
> On 28 January 2014 20:23, Alexandre Gramfort
> <alexandre.gramf...@telecom-paristech.fr> wrote:
> > I like the locality-sensitive hashing idea!
>
> +1
>
> we need to cleanup the GSOC idea wiki page...
>
> Alex
>
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