Oops, missed "Reply all" once again. Copying the message
Yes, the only need for such similarity learners is to use them in a
pipeline. It's especially convenient if one wants to do non-linear metric
learning using Kernel PCA trick. Then it'd be just another step in the
pipeline.
What do you mean by a generic transformer? In order to be usable in a
pipeline, it needs to be fit-able. Do you mean a wrapper like
OneVsRestClassifier?
The reason I included similarities is that I want to bring some supervision
into clustering by introducing meaningful metric. AFAIK, at the moment only
`AgglomerativeClustering` works well with a custom metric, and Spectral
Clustering and Affinity Propagation can work with a [n_samples, n_samples]
affinity matrix.
On Thu, Mar 26, 2015 at 12:08 PM, Mathieu Blondel <math...@mblondel.org>
wrote:
>
>
> 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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