Unsupervised Gene Selection For High Dimensional Data

In this paper, we present a new hybrid approach for unsupervised gene selection. Hybrid approaches try to utilize different evaluation criteria of the filter approaches and wrapper approaches in different search stages. Our method thus uses a two-step approach to identify informative genes. The first step retrieves gene subsets with original physical meaning based on their capacities to reproduce sample projections on principle components by applying the least-square-estimation based evaluation. The second step then searches for the best gene subsets that maximize clustering performance. When applied to a gene expression dataset of leukemia, the method identified a small set of genes whose expression is highly predictive