Feature-wise Competitive Repetition Suppression Learning for Gene Data Clustering and Feature Ranking

The paper extends Competitive Repetition-suppression (CoRe)
learning to deal with high dimensional data clustering. We show
how CoRe can be applied to the automatic detection of the unknown
cluster number and the simultaneous ranking of the features
according to learned relevance factors. The effectiveness of the
approach is tested on two freely available data sets from gene
expression data and the results show that CoRe clustering is able
to discover the true data partitioning in a completely
unsupervised way, while it develops a feature ranking that is
consistent with the state-of-the-art lists of gene relevance.