Feature Selection Package - Algorithms - Spectral Feature Selection
Spectral feature selection is a feature selection strategy that is largely based on the work of spectral graph theory. This is the unification of supervised and unsupervised feature selection.
Method Signature:
[ wFeat, SF ] = fsSpectrum( W, X, style, spec )

    wFeat: The weight of the features.
    SF: The statistical relevance of the features.
    W: The similarity matrix or a kernel matrix for the features.
    X: The input data, each row is an instance.
    style: Must be one of the following values: -1: use all, 0: use all except the 1st, k: use first k except 1st.
    spec: The spectral function to modify the eigen values.
Code Example
% Using the wine.dat data set, which can be found at
% [fspackage_location]/classifiers/knn/wine.mat
W = X*X';
Keyword in Evaluator Framework
BibTex entry for:

Spectral Feature Selection for Supervised and Unsupervised Learning by Zheng Zhao and Huan Liu.
  title = {Spectral Feature Selection for Supervised and Unsupervised Learning},
   author = {Liu, Huan and Zhao, Zheng},
   year = {2007},
   journal = {Proceedings of the 24th International Conference on Machine Learning}