On 03/18/2015 02:53 PM, Artem wrote:
I mean that if we were solving classification, we would have y that
tells us which class each example belongs to. So if we pass this
classification's ground truth vector y to metric learning's fit, we
can form S and D inside by saying that observations from the same
class should be similar.
Ah, I got it now.
Only being able to "transform" to a distance to the training set
is a bit limiting
Sorry, I don't understand what you mean by this. Can you elaborate?
The metric does not memorize training samples, it finds a (linear
unless kernelized) transformation that makes similar examples cluster
together. Moreover, since the metric is completely determined by a PSD
matrix, we can compute its square root, and use to transform new data
without any supervision.
Ah, I think I misunderstood your proposal for the transformer interface.
Never mind.
Do you think this interface would be useful enough? I can think of a
couple of applications.
It would definitely fit well into the current scikit-learn framework.
Do you think it would make sense to use such a transformer in a pipeline
with a KNN classifier?
I feel that training both on the same labels might be a bit of an issue
with overfitting.
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