Eq. (7) shows that their method reduces to a standard SVM objective if you
prepare input data as given in Eq. (6). Using a LinearSVC will give you w
tilde in Eq. (5) and you can make predictions with Eq. (1). I said
something wrong in my previous email, the shape of X should be [n_samples,
n_features * (n_clusters + 1)].
M.
On Mon, May 26, 2014 at 7:28 PM, Kathy Hida <[email protected]> wrote:
>
> I did send to the author, but he does not reply.
>
> Yes, I've implemented the normalization and k-means using scikit.
>
> Can you please explain a bit about your proposed solution? It is
> completely different from what I was thinking it should be.
>
> The paper suggests to subtract the global classifier from each local
> classifier (a classifier for each cluster), that is (w_cluster - w). how is
> this idea induced by your suggested solution? And why do you multiply the
> number of features by n_clusters+1?
>
>
> Thank you for the reply Mathieu, it is appreciated!
>
>
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