Ok, I failed to notice that in a Pareto efficiency sense. But it makes
sense to try the approach used in Annoy, when the maintainability of the
code is considered.
Apart from that, as I have mentioned earlier, the only LSH based ANN method
used in FLANN is usable for matching binary features using Hamming
distances. Using that wont be much of a use for scikit-learn.
So in my opinion, it is better give a high priority to LSH forest based ANN
when doing prototyping.
On Mon, Mar 17, 2014 at 12:55 AM, Olivier Grisel
<[email protected]>wrote:
> 2014-03-16 20:19 GMT+01:00 Maheshakya Wijewardena <[email protected]
> >:
> > Yes, I was considering the accuracy as well when speaking of the
> > performance. (Actually put more weight to that)
>
> Even though, on most plots the FLANN points are dominating the Annoy
> plots (in a Pareto-optimal sense).
>
> But the speed difference is not large enough to justify the added
> complexity of FLANN IMHO.
>
> --
> Olivier
>
>
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--
Undergraduate,
Department of Computer Science and Engineering,
Faculty of Engineering.
University of Moratuwa,
Sri Lanka
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