Hi Andy,
Adaptive Affinity Propagation is essentially an additional optimisation
layer on top of the original Affinity Propagation algorithm.
Affinity Propagation algorithm works off the similarity matrix and tries to
identify a number of data points that would be "centres" of clusters. The
behaviour of Affinity Propagation algorithm is governed by two parameters:
preferences (a vector of n_samples size) and damping.
The preferences on one hand are the way to incorporate prior knowledge
about likely cluster centres, on the other hand they control the number of
clusters produced by the algorithm. When there is no prior knowledge,
preferences are set to the same value for all sample points. The general
relationship between the preference value and the number of clusters is;
the greater the value the greater the number of clusters. The authors of
the Affinity Propagation algorithm recommend using the median similarity
value, but in the end one has to find the right preference value for each
new clustering problem.
The damping parameter defines speed at which the algorithm updates its
responsibility/availability evidence. The higher the damping parameter is
the less the algorithm prone to oscillations, but this slows down
convergence.
The Wang's solution is to run Affinity Propagation algorithm starting with
quite high preference value (like .5 of median similarity). As it
converges, the goodness of clustering is measured (they suggested
Silhouette index) the preference is decreased, and these steps are repeated
until the algorithm produces some minimal number of clusters. Along with
that, the presence of oscillations is monitored and should they appear they
are controlled by adjusting the damping parameter, should it reaches
maximum value by reducing the preference value.
The pdf in arXiv is the English translation of the original paper published
in Chinese.
I agree, Adaptive Affinity Propagation is not as widely used method as
defined in FAQ, I should have looked in it beforehand. May be it can be
considered a clear-cut improvement of the Affinity Propagation algorithm?
Any way if it is not to be added in sklearn, I am quite happy to release it
via PyPI.
Best wishes,
ilya
2014-12-02 14:34 GMT+00:00 Andy <t3k...@gmail.com>:
> Hi Ilya.
>
> Thanks for your interest in contributing.
> I am not expert in affinity propagation, so it would be great if you could
> give some details of what the advantage of the method is.
> The reference paper seems to be an arxiv preprint with 88 citations, which
> would probably not qualify for inclusion in scikit-learn,
> see the FAQ
> http://scikit-learn.org/dev/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published
>
> It might be a candidate for an external experimental / contribution
> project, which has been an idea that has been floating around for a while.
>
> Cheers,
> Andy
>
>
>
> On 12/02/2014 09:06 AM, Илья Патрушев wrote:
>
> Hi everybody,
>
> As far as I am aware, there is no adaptive affinity propagation
> clustering algorithm implementation in neither the stable nor the
> development version of sklearn.
> I have recently implemented the adaptive affinity propagation algorithm
> as a part of my image analysis project. I based my implementation on the
> paper by Wang et al., 2007 and their Matlab code, and sklearn's affinity
> propagation algorithm. This is not exactly a port of Matlab code since I
> have slightly modified the Wang's approach to deal with oscillations and
> added an optional upper limit on number of clusters.
> I am planning to submit the code to sklearn eventually. So please let me
> know if anybody already works on the algorithm, as we could join our
> efforts and save some time.
>
> Best wishes,
> ilya.
>
> --
> Ilya Patrushev, PhD.
> MRC National Institute for Medical Research
> The Ridgeway
> Mill Hill
> London NW7 1AA
> UK
> Tel: 0208 816 2656
> Fax: 0208 906 4477
>
>
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--
Ilya Patrushev,
MRC National Institute for Medical Research
The Ridgeway
Mill Hill
London NW7 1AA
UK
Tel: 0208 816 2656
Fax: 0208 906 4477
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