Discriminant Analysis via Support Vectors

Discriminant Analysis via Support Vectors codes

Description

In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition.

• MATLAB release: MATLAB 7.4 (R2007a)

[shahnaz fatima]

sir i am unable to open the svmtrain file.
it is a word file.
but not opening.
please help

[Michel Kocher]

Could you please provide the C code of the function svmtrsain or at least the mex version for MaOS.
Thank's

Michel Kocher