<p dir="auto">Is there any way to combine two different kernels in scikit-learn for different dimensions?<br>
For example, if there is two input dimensions in GP, is there any way to define:</p>
<p dir="auto">K((x,y),(x',y') = k(x,x') * k(y,y')<br>
More precisely, how can we define active dimension for each of them?<br>
Thanks for consideration.</p>
<p dir="auto">When doing kernel engineering, you often want different parts of the kernel to operate on different features (like some on time and some on space and some on whatever).<br>
From what I can see, this is not currently possible with sklearn.<br>
GPy implements that with a <code class="notranslate">active_dims</code> option for each kernel.<br>
That seems relatively easy, and I think it would be pretty cool. ping <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/jmetzen/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/jmetzen">@jmetzen</a> (also <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/jnothman/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/jnothman">@jnothman</a> and <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/GaelVaroquaux/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/GaelVaroquaux">@GaelVaroquaux</a> for the voice of reason?)</p>