lfda
lfda: Local Fisher Discriminant Analysis in R - Published in JOSS (2019)
Science Score: 93.0%
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Published in Journal of Open Source Software
Keywords
Repository
Local Fisher Discriminant Analysis in R
Basic Info
Statistics
- Stars: 76
- Watchers: 19
- Forks: 14
- Open Issues: 0
- Releases: 4
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Metadata Files
README.md
Note: This package has been maintained by @terrytangyuan since 2015. Please consider sponsoring!
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 and here. 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.
Welcome any feedback and pull request.
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.
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 thepredictmethod for klfda is still under development. Theplotmethod works the same way as inlfda`.
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 methodspredictandplotwork the same way as inlfda`.
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 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 first. Please note that this project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Contact
Contact the maintainer of this package: Yuan Tang terrytangyuan@gmail.com
Owner
- Name: Yuan Tang
- Login: terrytangyuan
- Kind: user
- Location: USA
- Company: Red Hat
- Website: https://terrytangyuan.github.io/about/
- Twitter: TerryTangYuan
- Repositories: 141
- Profile: https://github.com/terrytangyuan
Principal Engineer at Red Hat | Project Lead @argoproj @kubeflow | Maintainer @tensorflow XGBoost | ML/AI Infrastructure | Author | Speaker | Technical Advisor
JOSS Publication
lfda: Local Fisher Discriminant Analysis in R
Authors
Ant Financial Services Group, San Mateo, CA 94402, USA
Purdue University, West Lafayette, IN 47907, USA
Editor
Arfon Smith
Tags
distance metric learning statistics clustering machine learning dimensionality reduction local fisher discriminant analysisPapers & Mentions
Total mentions: 1
Tissue and regional expression patterns of dicistronic tRNA–mRNA transcripts in grapevine (Vitis vinifera) and their evolutionary co-appearance with vasculature in land plants
- DOI: 10.1038/s41438-021-00572-5
- OpenAlex ID: https://openalex.org/W3165335875
- Published: June 2021
GitHub Events
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Last synced: 7 months ago
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| Name | Commits | |
|---|---|---|
| terrytangyuan | t****n@g****m | 104 |
| Zach Mayer | z****r@g****m | 7 |
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Last synced: 6 months ago
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- Average time to close issues: 2 months
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Past Year
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- strengejacke (6)
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- Total packages: 2
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Total downloads:
- cran 1,649 last-month
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Total dependent packages: 4
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Total dependent repositories: 8
(may contain duplicates) - Total versions: 6
- Total maintainers: 1
cran.r-project.org: lfda
Local Fisher Discriminant Analysis
- Homepage: https://github.com/terrytangyuan/lfda
- Documentation: http://cran.r-project.org/web/packages/lfda/lfda.pdf
- License: MIT + file LICENSE
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Latest release: 1.1.3
published over 6 years ago
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conda-forge.org: r-lfda
- Homepage: https://github.com/terrytangyuan/lfda
- License: MIT
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Latest release: 1.1.3
published over 6 years ago
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Dependencies
- R >= 3.1.0 depends
- grDevices * imports
- plyr * imports
- rARPACK * imports
- rgl * suggests
- testthat * suggests