As a starter, I was thinking of implementing following plain LSH algorithm.
But as Daniel Vainsencher has showed (In the other mailing thread) we can
use LSH forest.
In regular LSH, a particular setting of the number of hash functions per
> index (k) and the number of indexes (L) essentially deter
>
> I would also rather focus on non-binary representations.
Even when using Random Projection method for hashing, only sign of the
result of dot product is considered. So that, in that situation also, there
will be a binary representation( or +1s and -1s). What is your idea about
this method?
N
On 02/26/2014 10:13 AM, Maheshakya Wijewardena wrote:
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
I would also rather focus on n
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> wr
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 fair
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,
Fac
