Subspace techniques for large-scale feature selection

A novel feature selection algorithm is presented which outperforms the well-known SFS (sequential forward selection) and SBS (sequential backward selection) algorithms for large-scale problems. The approach utilizes the solution to the similar problem of large-scale feature extraction by choosing a subset of the original measurements that are closest to the space spanned by the extracted (transformed) features. The authors develop a computationally efficient Frobenius subspace distance metric for the subspace comparisons, which reduces the complexity from order N taken k at a time to order N/sup 3/ operations. Finally, sufficient conditions for optimality of the algorithm are presented that demonstrate the relationship between the feature extraction and the feature selection solutions.<>