KLFDAM {DA}

Kernel local Fisher discriminant analysis

Description

This function performs Kernel Local Fisher Discriminant Analysis. The function provided here allows users to carry out the KLFDA using a pairwise matrix. We used the gaussan matrix as example. Users can compute different kernel matrix or distance matrix as the input for this function.

Usage

KLFDAM(kdata, y, r,
metric = c("weighted", "orthonormalized", "plain"),
tol=1e-5,knn = 6, reg = 0.001)

Arguments

kdata

The input dataset (kernel matrix). The input data can be a genotype matrix, dataframe, species occurence matrix, or principal components. The dataset have to convert to a kernel matrix before feed into this function.

y

The group lables

r

Number of reduced features

metric

Type of metric in the embedding space (default: 'weighted') 'weighted' - weighted eigenvectors 'orthonormalized' - orthonormalized 'plain' - raw eigenvectors

knn

The number of nearest neighbours

tol

Tolerance to avoid singular values

reg

The regularization parameter

Details

Kernel Local Fisher Discriminant Analysis for any kernel matrix. It was proposed in Sugiyama, M (2006, 2007) as a non-linear improvement for discriminant analysis. This function is adopted from Tang et al. 2019.

Value

Z

The reduced features

Tr

The transformation matrix

References

Tang, Y., & Li, W. (2019). lfda: Local Fisher Discriminant Analysis inR. Journal of Open Source Software, 4(39), 1572.

Sugiyama, M (2007). Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. Journal of Machine Learning Research, vol.8, 1027-1061.

Sugiyama, M (2006). Local Fisher discriminant analysis for supervised dimensionality reduction. In W. W. Cohen and A. Moore (Eds.), Proceedings of 23rd International Conference on Machine Learning (ICML2006), 905-912.

See Also

KLFDA

Examples

kmat <- kmatrixGauss(iris[, -5],sigma=1)
zklfda=KLFDAM(kmat, iris[, 5], r=3,metric = "plain",tol=1e-5 )
print(zklfda$Z)