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

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

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)

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:

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

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.

Examples

Run this code

# NOT RUN {
x <- iris[, -5]
y <- iris[, 5]
self(x, y, beta = 0.1, r = 3, metric = "plain")
# }