DA (version 1.2.0)

KLFDAM: 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

Value

Z

The reduced features

Tr

The transformation matrix

%% ...

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.

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

# NOT RUN {
kmat <- kmatrixGauss(iris[, -5],sigma=1)
zklfda=KLFDAM(kmat, iris[, 5], r=3,metric = "plain",tol=1e-5 )
print(zklfda$Z)
# }