There are several distinct usages:
- kernel density estimation
- kernel trick
- kernel smoothing
Please explain what the "kernel" in them means, in plain English, in your own words.
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Sign up to join this communityThere are several distinct usages:
Please explain what the "kernel" in them means, in plain English, in your own words.
In both statistics (kernel density estimation or kernel smoothing) and machine learning (kernel methods) literature, kernel is used as a measure of similarity. In particular, the kernel function $k(x,.)$ defines the distribution of similarities of points around a given point $x$. $k(x,y)$ denotes the similarity of point $x$ with another given point $y$.
There appear to be at least two different meanings of "kernel": one more commonly used in statistics; the other in machine learning.
In statistics "kernel" is most commonly used to refer to kernel density estimation and kernel smoothing.
A straightforward explanation of kernels in density estimation can be found (here).
In machine learning "kernel" is usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem "by mapping the original non-linear observations into a higher-dimensional space".
A simple visualisation might be to imagine that all of class $0$ are within radius $r$ of the origin in an x, y plane (class $0$: $x^2 + y^2 < r^2$); and all of class $1$ are beyond radius $r$ in that plane (class $1$: $x^2 + y^2 > r^2$). No linear separator is possible, but clearly a circle of radius $r$ will perfectly separate the data. We can transform the data into three dimensional space by calculating three new variables $x^2$, $y^2$ and $\sqrt{2}xy$. The two classes will now be separable by a plane in this 3 dimensional space. The equation of that optimally separating hyperplane where $z_1 = x^2, z_2 = y^2$ and $z_3 = \sqrt{2}xy$ is $z_1 + z_2 = 1$, and in this case omits $z_3$. (If the circle is off-set from the origin, the optimal separating hyperplane will vary in $z_3$ as well.) The kernel is the mapping function which calculates the value of the 2-dimensional data in 3-dimensional space.
In mathematics, there are other uses of "kernels", but these seem to be the main ones in statistics.