In summary, I think this does look like a good basis for a proposal :)


On 03/18/2015 05:14 PM, Artem wrote:

    ​
    Do you think this interface would be useful enough?

​One of mentioned methods (LMNN) actually uses prior knowledge in exactly the same way, by comparing labels' equality. Though, it was designed to facilitate KNN. ​
​Authors of the other one (ITML) explicitly mention in the paper that one can construct those sets S and D from labels.

    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

Pipelining looks like a good way to combine these methods, but overfitting could be a problem, indeed.
Not sure how severe it can be.

On Wed, Mar 18, 2015 at 10:07 PM, Andreas Mueller <t3k...@gmail.com <mailto:t3k...@gmail.com>> wrote:


    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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