also, using CCA on a 1D Y is the same as linear regression. So you probably
do that instead
On Tuesday, October 20, 2015, Arthur Mensch <arthur.men...@inria.fr> wrote:
> Hi Dai,
>
> CCA finds the vectors in x space and Y space that maximizes the
> correlation corr(u' X, v' Y), and continues finding such vectors under the
> constrain that (u_i)_i, (v_i)_i are orthogonal.
>
> As in your case dim Y = 1, you can only set n_components = 1: the vector v
> will be [1], and u will be the linear combination u that maximizes corr(u'
> X, Y). I guess it should be in the doc.
>
> You cannot find more than one pair of vector (u, v) as v is already a
> basis of Y space. Y variability is entirely explained with (u, v) only,
> hence the warning.
> Le 20 oct. 2015 06:52, "Dai Yan" <kanshu...@gmail.com
> <javascript:_e(%7B%7D,'cvml','kanshu...@gmail.com');>> a écrit :
>
>> Hello,
>>
>>
>> I hope use CCA(Canonical Correlation Analysis) to fit problem set with
>> size of (35000, 117) to its label (35000, 1), 35000 is samples and 117 is
>> feature dimension per sample.
>>
>> Now I have the following two problems.
>>
>> 1) How to choose appropriate CCA n_compoents parameter to fix my samples?
>>
>>
>>
>> 2) When classifying with n_components = 1 or n_components = 2, the fit
>> procedure quits with the following messages,
>>
>> /usr/local/lib/python2.7/dist-packages/sklearn/cross_decomposition/pls_.py:277:
>> UserWarning: Y residual constant at iteration 1
>> warnings.warn('Y residual constant at iteration %s' % k)
>>
>> And here I paste some of my codes to show CCA initialization parameters.
>>
>> *cca = CCA(max_iter=500000, tol=1e-05)*
>> *cca.fit(features, labels) # features.shape = [35000, 117], labels =
>> [35000, 1]*
>>
>>
>> Could you give me some hints on this?
>>
>>
>> Thanks,
>>
>> Yan
>>
>>
>>
>>
>>
>>
>>
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