9 releases (5 breaking)
0.7.0 | Oct 16, 2023 |
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0.6.1 | Dec 3, 2022 |
0.6.0 | Jun 15, 2022 |
0.5.1 | Mar 1, 2022 |
0.2.1 | Nov 29, 2020 |
#652 in Machine learning
2,063 downloads per month
Used in 4 crates
(3 directly)
290KB
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
~5MB
~96K SLoC