An adaptive feature selection algorithm based on MDS with uncorrelated constraints for tumor gene data classification

Abstract

The developing of DNA microarray technology has made it possible to study the cancer in view of the genes. Since the correlation between the genes is unconsidered, current unsupervised feature selection models may select lots of the redundant genes during the feature selecting due to the over focusing on genes with similar attribute. which may deteriorate the clustering performance of the model. To tackle this problem, we propose an adaptive feature selection model here in which reconstructed coefficient matrix with additional constraint is introduced to transform original data of high dimensional space into a low-dimensional space meanwhile to prevent over focusing on genes with similar attribute. Moreover, Alternative Optimization (AO) is also proposed to handle the nonconvex optimization induced by solving the proposed model. The experimental results on four different cancer datasets show that the proposed model is superior to existing models in the aspects such as clustering accuracy and sparsity of selected genes.

Keywords

• unsupervised feature selection
• gene expression data
• cancer classification
• uncorrelated constraint
• structure learning