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

• "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.1027-1061, 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.905-912, Pittsburgh, Pennsylvania, USA, Jun. 25-29, 2006.
  [ paper, slides ]