7 releases (4 breaking)

0.6.0 Jun 15, 2022
0.5.1 Mar 1, 2022
0.5.0 Oct 21, 2021
0.4.0 Apr 28, 2021
0.2.1 Nov 29, 2020

#222 in Machine learning

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432 downloads per month
Used in 4 crates (3 directly)

MIT/Apache

285KB
5.5K SLoC

Kernel methods

linfa-kernel provides methods for dimensionality expansion.

The Big Picture

linfa-kernel is a crate in the linfa ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python's scikit-learn.

In machine learning, 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.

License

Dual-licensed to be compatible with the Rust project.

Licensed under the Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0 or the MIT license http://opensource.org/licenses/MIT, at your option. This file may not be copied, modified, or distributed except according to those terms.

Dependencies

~4MB
~83K SLoC