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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> security intelligence. It gives you real-time visual feedback on key
> security issues and trends. Skip the complicated setup - simply import
> a virtual appliance and go from zero to informed in seconds.
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