Fast orthogonal forward selection algorithm for feature subset selection.

Feature selection is an important issue in pattern classification. In the presented study, we develop a fast orthogonal forward selection (FOFS) algorithm for feature subset selection. The FOFS algorithm employs an orthogonal transform to decompose correlations among candidate features, but it performs the orthogonal decomposition in an implicit way. Consequently, the fast algorithm demands less computational effort as compared with conventional orthogonal forward selection (OFS).