I'm studying the task of feature selection on biological microarray, thats is high dimensional dataset (thousands of features) with small number of data points (lees than one hundred). This feature selection serves to support supervised classification task.
What research or algorithm you can recommend me?
For now I learn about permutation-based algorithm, but I'm open to any advice. I need real research paper to understand how it works, I'm not looking for pre-packed programs.
EDIT1: I have read 2-3 article dealing of feature selection based on correlation between feature. The central idea of this article is to remove the correlated feature because bring redundant information. For me this is not be a good idea because some classifiers could exploit the dependence between freature (Like SVM or Decsion Tree). What you think about?
EDIT2: Is there a specific term to describe a High Dimensional Dataset with a few number of osservation like microarray? because I need it for improve my search on Google Scholar.