O btw, you are right in your application,
Since you are able to capture more non-linearities, a similarity matrix for
time series data or a functional data dataset can be computed with this
non-linear correlation coefficient.
2012/9/3 Analytic Bastard <[email protected]>
> Hi Olivier,
>
> Yes that is the coefficient/statistic. Fukumizu, Gretton and coworkers
> also have one based on RKHS covariance operators.
>
> It would be useful to test whether any classification or regression
> problem can be tractable. After all, if two variables are independent, it
> won't be possible to try to classify or do regression with them.
>
> In any case, I am willing to implement any desired feature that is already
> listed.
>
>
>
>
> 2012/9/3 Olivier Grisel <[email protected]>
>
>> 2012/9/3 Analytic Bastard <[email protected]>:
>> > Hi guys,
>> >
>> > My name is Javier and I am a machine learning researcher.
>> >
>> > Currently, I am learning Python and found out this excellent package. I
>> > would like to contribute by implementing any planned feature and by
>> > implementing some ideas such as a non-linear correlation coefficient by
>> > SZÉKELY and RIZZO.
>>
>> Hi Javier,
>>
>> I assume you are referring to
>> http://en.wikipedia.org/wiki/Distance_correlation ?
>>
>> If so it could probably fit in the sklearn.metrics.pairwise module. Do
>> you have any application in mind? Would this useful for building an
>> affinity matrix for clustering of time series data for instance?
>>
>> --
>> Olivier
>> http://twitter.com/ogrisel - http://github.com/ogrisel
>>
>>
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>
>
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