In this paper, we identify two issues involved in developing an automated feature subset selection algorithm for unlabeled data: the need for finding the number of clusters in conjunction with feature selection, and the need for normalizing the bias of feature selection criteria with respect to dimension. We explore the feature selection problem and these issues through FSSEM (Feature Subset Selection using Expectation-Maximization (EM) clustering) and through two different performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood. We present proofs on the dimensionality biases of these feature criteria, and present a cross-projection normalization scheme that can be applied to any criterion to ameliorate these biases. Our experiments show the need for feature selection, the need for addressing these two issues, and the effectiveness of our proposed solutions.
For example, Dy and Brodley proposed a cluster-based method REF , which explores the feature selection problem through FSSEM (Feature Subset Selection using Expectation-Maximization (EM) clustering) and two different performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood.
Feature Selection for Unsupervised Learning
J. Mach. Learn. Res.
J. Mach. Learn. Res.
Mathematics Computer Science