lfda (version 1.1.1)

self: Semi-Supervised Local Fisher Discriminant Analysis(SELF) for Semi-Supervised Dimensionality Reduction

Description

Performs semi-supervised local fisher discriminant analysis (SELF) on the given data. SELF is a linear semi-supervised dimensionality reduction method smoothly bridges supervised LFDA and unsupervised principal component analysis, by which a natural regularization effect can be obtained when only a small number of labeled samples are available.

Usage

self(X, Y, beta = 0.5, r, metric = c("orthonormalized", "plain", "weighted"), kNN = 5, minObsPerLabel = 5)

Arguments

X

n x d matrix of original samples. n is the number of samples.

Y

length n vector of class labels

beta

degree of semi-supervisedness (0

r

dimensionality of reduced space (default: d)

metric

type of metric in the embedding space (no default) 'weighted' --- weighted eigenvectors 'orthonormalized' --- orthonormalized 'plain' --- raw eigenvectors

kNN

parameter used in local scaling method (default: 5)

minObsPerLabel

the minimum number observations required for each different label(default: 5)

Value

list of the SELF results:

T

d x r transformation matrix (Z = x * T)

Z

n x r matrix of dimensionality reduced samples

References

Sugiyama, Masashi, et al (2010). Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Machine learning 78.1-2: 35-61.

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

See lfda for LFDA and klfda for the kernelized variant of LFDA (Kernel LFDA).

Examples

## Not run: 
# X <- iris[,-5]
# Y <- iris[,5]
# result <- self(X,Y,beta = 0.1, r = 3, metric = "plain")
# ## End(Not run)