R: Semi-Supervised Local Fisher Discriminant Analysis(SELF) for...
self
R Documentation
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.
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
## Not run:
X <- iris[,-5]
Y <- iris[,5]
result <- self(X,Y,beta = 0.1, r = 3, metric = "plain")
## End(Not run)