Crate linfa_kernel

Kernel methods

Kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine. They owe their name to the kernel functions, which maps the features to some higher-dimensional target space. Common examples for kernel functions are the radial basis function (euclidean distance) or polynomial kernels.

Current State

linfa-kernel currently provides an implementation of kernel methods for RBF and polynomial kernels, with sparse or dense representation. Further a k-neighbour approximation allows to reduce the kernel matrix size.

Low-rank kernel approximation are currently missing, but are on the roadmap. Examples for these are the Nyström approximation or Quasi Random Fourier Features.

Re-exports

• pub use inner::Inner;
• pub use inner::KernelInner;

Modules

• inner

Structs

• KernelBase
  A generic kernel
• KernelParams
  Defines the set of parameters needed to build a kernel

Enums

• KernelMethod
  The inner product definition used by a kernel.
• KernelType
  Kernel representation, can be either dense or sparse

Type Aliases

• Kernel
  Type definition of Kernel that owns its inner matrix
• KernelView
  Type definition of Kernel that borrows its inner matrix