On Thu, Mar 26, 2015 at 5:49 PM, Artem <barmaley....@gmail.com> wrote:
> 1. Right, forgot to add that parameter. Well, I can apply an RBF kernel to
> get a similarity matrix from a distance matrix inside transform.
>
> 2. Usual transformer returns neither distance, nor similarity, but
> transforms the input space so that usual Euclidean distance acts like the
> learned Mahalanobis.
>
I'd really try to avoid duplicating all classes. As you said the Euclidean
distance can be used on the transformed data. So we can get a similarity
matrix in just two lines:
X_transformed = LMNN().fit_transform(X, y)
S = -euclidean_distances(X_transformed)
The only benefit I see of being able to transform to a similarity matrix is
for pipelines. This can be done as I said using a generic transformer X ->
S. However, I am not completely sure this is even needed since all our
algorithms work on X of shape [n_samples, n_features] by default.
M.
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