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.packages('lfda')`

`devtools::install_github('terrytangyuan/lfda')`

Please call `citation("lfda")`

in R to properly cite this
software. A white paper is published on Journal of Open Source Software
here.

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:

```
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
```

The main usage is the same except for an additional
`kmatrixGauss`

call to the original data set to perform a
kernel trick:

```
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`

.

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:

```
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 and scroll down
to the last section for an example.

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 the maintainer of this package: Yuan Tang terrytangyuan@gmail.com