Abstract: High dimensional data is often encountered in many data analysis tasks.Feature selection techniques aim to find the most representative features from the original high-dimensional data.Due to the lack of class label information,it is much more difficult to select suitable features in unsupervised learning scenarios than in supervised scenarios.Traditional unsupervised feature selection methods usually score the features of samples according to certain criteria in which samples are treated indiscriminately.However,these approaches cannot capture the internal structure of data completely.The importance of different samples should vary.There is a dual relationship between weight of sample and feature that will influence each other.Therefore,an unsupervised feature selection algorithm based on dual manifold re-ranking(DMRR) is proposed in this paper.Different similarity matrices are constructed to depict the manifold structures on samples and samples,features and features,and samples and features respectively.Then manifold re-ranking is carried out by combining the initial scores of samples and features.By comparing DMRR with three original unsupervised feature selection algorithms and two unsupervised feature selection post-processing algorithms,experimental results verify that importance information of different samples and the dual relationship between sample and feature are helpful to achieve better feature selection.

Key words: Dual, Manifold learning, Re-ranking, Feature selection, Unsupervised learning