Scaling-Up Quadratic Programming Feature Selection

Domains such as vision, bioinformatics, web search and web rankings involve datasets where number of features is very large. Feature selection is commonly employed to deal with high dimensional data. Recently, Quadratic Programming Feature Selection (QPFS) has been shown to outperform many of the existing feature selection methods for a variety of datasets. In this paper, we propose a Sequential Minimal Optimization (SMO) based framework for QPFS. This helps in reducing the cubic computational time (in terms of data dimension) of the standard QPFS to quadratic time in practice. Further, our approach has significantly less memory requirement than QPFS. This memory saving can be critical for doing feature selection in high dimensions. The performance of our approach is demonstrated using three publicly available benchmark datasets from bioinformatics domain.