Kernel Methods for Clustering
Kernel Methods approach the problem by mapping the data into a high
dimensional feature space, where each coordinate corresponds to one
feature of the data items, transforming the data into a set of points
in a Euclidean space. In that space, a variety of methods can be used
to find relations in the data. Since the mapping can be quite general
(not necessarily linear, for example), the relations found in this way
are accordingly very general. This approach is called the kernel trick.
More information on Wikipedia.
Kernel Parameters:
- Kernel Type:
- Linear: linear kernel
- Polynomial: polynomial kernel
- RBF: radial basis function (gaussian) kernel
- Kernel Width: inverse variance for the kernel function, determines the radius of influence of each sample (RBF + Poly)
- Degree: degree of the polynomial (Poly)
Methods:
- One-Class SVM: computes likelihood of data belonging to a cluster (shown in red)
- nu: penalty parameter (computes nu automatically)
- Kernel K-Means: replaces the K-Means distance metric with the kernel function
- clusters: number of clusters