self {lfda}

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 <= beta <= 1; default is 0.5 ) 0: totally supervised (discard all unlabeled samples) 1: totally unsupervised (discard all label information)

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

Author(s)

Yuan Tang

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

x <- iris[, -5]
y <- iris[, 5]
self(x, y, beta = 0.1, r = 3, metric = "plain")