Privacy preserving feature selection for distributed data using virtual dimension

Abstract

Data Mining often suffers from the curse of dimensionality. Huge numbers of dimensions or attributes in the data pose serious problems to the data mining tasks. Traditionally data dimensionality reduction techniques like Principal Component Analysis have been used to address this problem.However, the need might be to remain in the original attribute space and identify the key predictive attributes instead of moving to a transformed space. As a result feature subset selection has become an important area of research over the last few years.