Local Fisher Discriminant Analysis (LFDA)
Description
Local Fisher Discriminant Analysis (LFDA)
is a linear supervised dimensionality reduction method and is particularly useful
when some class consists of separate clusters.
LFDA has an analytic form of the embedding matrix
and the solution can be easily computed just by solving
a generalized eigenvalue problem.
Therefore, LFDA is scalable to large datasets and
computationally reliable.
A kernelized variant of LFDA called Kernel LFDA (KLFDA) is available from
here.
Download
MATLAB implementation of LFDA:
LFDA.tgz
 "LFDA.m" is the main function.
 "demo_LFDA.m" is a demo script.
Examples
References

Sugiyama, M.
Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis.
Journal of Machine Learning Research,
vol.8 (May), pp.10271061, 2007.
[
paper (pdf)
]

Sugiyama, M.
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),
pp.905912, Pittsburgh, Pennsylvania, USA, Jun. 2529, 2006.
[
paper,
slides
]
Masashi Sugiyama
(sugi [at] cs.titech.ac.jp)
Sugiyama Laboratory,
Department of Computer Science,
Graduate School of Information Science and Engineering,
Tokyo Institute of Technology,
2121W874, Ookayama, Meguroku, Tokyo, 1528552, Japan.
TEL & FAX: +81357342699