Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/terrytangyuan/lfda
Local Fisher Discriminant Analysis in R
https://github.com/terrytangyuan/lfda
dimensionality-reduction distance-metric-learning machine-learning metric-learning r statistics
Last synced: 10 days ago
JSON representation
Local Fisher Discriminant Analysis in R
- Host: GitHub
- URL: https://github.com/terrytangyuan/lfda
- Owner: terrytangyuan
- License: other
- Created: 2015-08-10T20:58:19.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2023-07-07T16:53:11.000Z (over 1 year ago)
- Last Synced: 2024-10-23T11:48:26.480Z (18 days ago)
- Topics: dimensionality-reduction, distance-metric-learning, machine-learning, metric-learning, r, statistics
- Language: R
- Homepage:
- Size: 111 KB
- Stars: 77
- Watchers: 20
- Forks: 14
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
**Note**: This package has been maintained by [@terrytangyuan](https://github.com/terrytangyuan) since 2015. Please [consider sponsoring](https://github.com/sponsors/terrytangyuan)!
[](https://coveralls.io/r/terrytangyuan/lfda?branch=master)
[](https://cran.r-project.org/package=lfda)
[](https://cran.r-project.org/package=lfda)
[](http://badges.mit-license.org)
[](https://doi.org/10.21105/joss.01572)
[](https://doi.org/10.5281/zenodo.3352038)
# lfda
R package for performing and visualizing *Local Fisher Discriminant Analysis*, *Kernel Local Fisher Discriminant Analysis*, and *Semi-supervised Local Fisher Discriminant Analysis*. It's the first package with those methods implemented in native R language. It also provides visualization functions to easily visualize the dimension reduction results.
Introduction to the algorithms and their application can be found [here](https://www.gastrograph.com/resources/local-fisher-discriminant-analysis-on-beer-style-clustering) and [here](http://www.ms.k.u-tokyo.ac.jp/software.html#LFDA). These methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems. An introduction to the package is also available in Chinese [here](https://cosx.org/2015/08/a-brief-description-of-the-method-and-the-algorithm-of-the-lfda-package/).
Welcome any [feedback](https://github.com/terrytangyuan/lfda/issues) and [pull request](https://github.com/terrytangyuan/lfda/pulls).
## Install the current release from CRAN:
```{R}
install.packages('lfda')
```
## Install the latest development version from github:
```{R}
devtools::install_github('terrytangyuan/lfda')
```
## Citation
Please call `citation("lfda")` in R to properly cite this software. A white paper is published on Journal of Open Source Software [here](http://joss.theoj.org/papers/10.21105/joss.01572).
## Examples
### Local Fisher Discriminant Analysis(LFDA)
Suppose we want to reduce the dimensionality of the original data set (we are using `iris` data set here) to 3, then we can run the following:
```{R}
k <- iris[,-5] # this matrix contains all the predictors to be transformed
y <- iris[,5] # this should be a vector that represents different classes
r <- 3 # dimensionality of the resulting matrix
# run the model, note that two other kinds metrics we can use: 'weighted' and 'orthonormalized'
model <- lfda(k, y, r, metric = "plain")
plot(model, y) # 3D visualization of the resulting transformed data set
predict(model, iris[,-5]) # transform new data set using predict
```
### Kernel Local Fisher Discriminant Analysis(KLFDA)
The main usage is the same except for an additional `kmatrixGauss` call to the original data set to perform a kernel trick:
```{R}
k <- kmatrixGauss(iris[,-5])
y <- iris[,5]
r <- 3
model <- klfda(k, y, r, metric = "plain")
```
Note that the `predict` method for klfda is still under development. The `plot` method works the same way as in `lfda`.
### Semi-supervised Local Fisher Discriminant Analysis(SELF)
This algorithm requires one additional argument such as `beta` that represents the degree of semi-supervisedness. Let's assume we ignore 10% of the labels in `iris` data set:
```{R}
k <- iris[,-5]
y <- iris[,5]
r <- 3
model <- self(k, y, beta = 0.1, r = 3, metric = "plain")
```
The methods `predict` and `plot` work the same way as in `lfda`.
### Integration with {ggplot2::autoplot}
`{ggplot2::autoplot}` has been integrated with this package. Now `{lfda}` can be plotted in 2D easily and beautifully using `{ggfortify}` package. Go to [this link](http://rpubs.com/sinhrks/plot_pca) and scroll down to the last section for an example.
## Contribute & Code of Conduct
To contribute to this project, please take a look at the [Contributing Guidelines](https://github.com/terrytangyuan/lfda/blob/master/CONTRIBUTING.md) first. Please note that this project is released with a [Contributor Code of Conduct](https://github.com/terrytangyuan/lfda/blob/master/CONDUCT.md). By contributing to this project, you agree to abide by its terms.
## Contact
Contact the maintainer of this package:
Yuan Tang