The method "Bit sampling for Hamming distance" is already included in
"brute" algorithm as the metric "hamming" in Nearest neighbor search.
Hence, I think that does not need to be implemented as a LSH algorithm.
On Wed, Feb 26, 2014 at 12:46 AM, Maheshakya Wijewardena <
pmaheshak...@gmail.com> wrote:
> Approximating Nearest neighbor search is one of the application of
> locality sensitive hashing.There are five major methods.
>
> - Bit sampling for Hamming distance
> - Min-wise independent permutations
> - Nilsimsa Hash
> - Random projection
> - Stable distributions
>
> Bit sampling method is fairly straight forward. A reference for the
> implementation of Random projection method can be taken from *lshash
> <https://pypi.python.org/pypi/lshash>* library.
> I'm looking forward to see comments for this from prospective mentors of
> this project.
>
> Thank you.
> Maheshakya.
>
>
>
> On Tue, Feb 25, 2014 at 8:24 AM, Maheshakya Wijewardena <
> pmaheshak...@gmail.com> wrote:
>
>> Hi,
>> I have looked into this project idea. I have studied this method and I
>> like to discuss further on this.
>> I would like to know who the mentors for this project are and to get some
>> insight on how to begin.
>>
>> Regards,
>> Maheshakya,
>> --
>> Undergraduate,
>> Department of Computer Science and Engineering,
>> Faculty of Engineering.
>> University of Moratuwa,
>> Sri Lanka
>>
>
>
>
> --
> Undergraduate,
> Department of Computer Science and Engineering,
> Faculty of Engineering.
> University of Moratuwa,
> Sri Lanka
>
--
Undergraduate,
Department of Computer Science and Engineering,
Faculty of Engineering.
University of Moratuwa,
Sri Lanka
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