Hi Ricardo.
I think you forgot to mention what [1] and [2] are.
What is the difference between a relative neighborhood graph and a
neighborhood graph?
To me that sounds a bit to special purpose for the moment.
We need Logistic Regression first (which might also be a good GSoC project)!
Just my opinion though ;)
Cheers,
Andy
On 03/22/2013 06:49 AM, Ricardo Corral C. wrote:
Ok, this is a brief description of what I'm interested in.
Recently, I faced a problem of evaluating the quality of a method to
obtain features from protein structures.
I adopted the approach given in [1] to measure separability of my
classes independently of my capacity of make good predictions.
This is basically a hypothesis testing of whether or not the
distribution of classes over feature vectors is somewhat random.
This test is made over the construction of a Relative Neighbourhood
Graph, which is O(n^3), thus, so prohibitive for practical use.
There is an efficient method for constructing RNG on the plane
described in [2] O(n*log(n)), but O(n^2) for a higher d dimension (in
fact O(n^2*f(d)) with f(d) <= (2*sqrt(d) +2)^d...).
Actually, I have the test implemented, and I'm refining a speedup of
RNG construction based on the Half-Space Proximal (HSP) graph. This is
O(n^2log(n)), and there is no dependence of dimension other than time
consumed in calculating distances.
This is made by doing RNG test over edges in HSP (attached images for
clarify this).
Could this be of interest for sklearn users? And if so, be considered for GSoC?
On Thu, Mar 21, 2013 at 12:02 PM, Andreas Mueller
<amuel...@ais.uni-bonn.de> wrote:
On 03/21/2013 06:56 PM, Ricardo Corral C. wrote:
I would like to contribute with an idea different from those listed.
Is this the place to describe my proposal?
I think posting it on the mailing list (at least a short description)
would be a good start.
Also starting to contribute ;)
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