Kernel Clustering
Overview
The kernel method is one way to take a linear regression and
turn it into a method that can utilize a nonlinear relationship
among the selected features, by first mapping the features
into a new feature space (in a nonlinear fashion).
The technique is similar to other kernel methods. By first
recasting the regression methodology in a format that utilizes
an embedded kernel function, the regression can be run
in which a different kernel function is used.
Effectively this technique retains the linear features of the model,
while placing the nonlinearities into the feature map using the kernel
function.
kernel density estimation - example1
{% \vec{x} \in \mathbb{R}^n %}
{% \phi %}
What you would seek to do, would be to recast the the regression
formula so that the values of the sample set only show up as part
of inner product, such as
{% \kappa(\vec{x_i}, \vec{x_j}) = \; < \phi(\vec{x_i}), \phi(\vec{x_j}) > %}
let dn = await import('/lib/machine-learning/kernel/v1.0.0/density.js');
let kl = await import('/lib/machine-learning/kernel/v1.0.0/kernel.js');
Try it!
