Local Fisher Discriminant Analysis (LFDA)


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.


MATLAB implementation of LFDA: LFDA.tgz



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,
2-12-1-W8-74, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan.
TEL & FAX: +81-3-5734-2699