On 13 March 2012 18:16, Andreas Mueller <[email protected]> wrote:
> On 03/13/2012 07:49 AM, Olivier Grisel wrote:
> > Le 12 mars 2012 17:49, Robert Layton<[email protected]> a écrit :
> >> I'll work off that template, and when I work out the details of the
> >> shrinking parameters (specifically which one is more in use), I'll
> branch
> >> and submit a PR.
> > Great. I think the nearest centroid is a very nice baseline classifier
> > for sanity check: fast to fit, fast to predict, zero hyper-paramete
> > and yet make reasonable assumption for many classification dataset (a
> > good example of high bias, low variance, the opposite of deep decision
> > trees or RBF kernels SVMs).
> >
> Have you ever compared it to LDA?
> I would think the results are quite similar.
>
>
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You were quite right Andreas. From the paper:
The discriminant scores in Eq.6 are similar to that used in linear
discriminant analysis LDA), which arises from using the Mahalanobis metric
in computing distance to centroids
I've submitted a PR without the shrinking, which is pretty much done. I'm
working through the paper now. It's fairly well described, so if I get
time, I should have the PR ready tomorrow.
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