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 non-binary representations.
There is no efficient way to work with binary data in numpy afaik -- at
least none that is supported in sklearn.
I'm very interested in this project but unfortunately I don't have the
time to mentor.
Cheers,
Andy
On Wed, Feb 26, 2014 at 12:46 AM, Maheshakya Wijewardena
<pmaheshak...@gmail.com <mailto: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 <mailto: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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