R package for performing and visualizing Local Fisher Discriminant Analysis, Kernel Local Fisher Discriminant Analysis, and Semi-supervised Local Fisher Discriminant Analysis.

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.

Welcome any feedback and pull request.

install.packages('lfda')

devtools::install_github('terrytangyuan/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:

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.