Hi,
Currently, unsupervised neighbor search is implemented in
sklearn.neighbors. There are three algorithms used to perform neighbor
search.
- kd tree
- ball tree
- brute force method
kd tree and ball tree are implemented separately as cython implementations.
Both of them use binary tree(binary_tree.pxi). In the NeighborBass class,
above implementations are used.
As Locality Sensitivity Hashing will also be used in approximating nearest
neighbors, its' implementation should also be a part of sklearn.neighbors.
But Locality sensitivity hashing algorithms do not involve binary tree
implementation. Therefore it has to be implemented separately and be used
in sklearn.neighbors as another algorithm.Is this approach correct?
I'm trying to implement a base LSH class and the hashing algorithms as
cython implementations. To approximate neighbor search using those
algorithms, multiple hash tables will be created with those hash functions
so, for that an efficient way to store is required.
These are some of the issues I found disturbing while I was trying to
implement LSH to approximate neighbor search. Is this an appropriate
approach to implement this or are there any other better ways?
Regards,
Maheshakya,
--
Undergraduate,
Department of Computer Science and Engineering,
Faculty of Engineering.
University of Moratuwa,
Sri Lanka
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