Features selection technics overview

There are two main approaches to this selection process:

• Filter approaches: you select the features first, then you use this subset to execute classification or clustering algorithms, etc;

• Embedded or Wrapper approaches a classification algorithm is applied to the raw dataset in order to identify the most relevant features.

This post describes several approaches. From correlation matrix to Principal Components Analysis (PCA). 

A good summary with examples in Biostat. 

http://www.r-bloggers.com/introduction-to-feature-selection-for-bioinformaticians-using-r-correlation-matrix-filters-pca-backward-selection/