LAUSR

Feature selection is a fundamental preprocess before performing actual learning; especially in unsupervised manner where the data are unlabeled. Essentially, when there are too many features in the problem, dimensionality reduction through discarding weak features is highly desirable. In this paper, we present a framework for unsupervised feature selection based on dependency maximization between the samples similarity matrices before and after deleting a feature. In this regard, a novel estimation of Hilbert–Schmidt independence criterion (HSIC), more appropriate for high-dimensional data with small sample size, is introduced. Its key idea is that by eliminating the redundant features and/or those have high inter-relevancy, the pairwise samples similarity is not affected seriously. Also, to handle the diagonally dominant matrices, a heuristic trick is used in order to reduce the dynamic range of matrix values. In order to speed up the proposed scheme, the gap statistic and k-means clustering methods are also employed. To assess the performance of our method, some experiments on benchmark datasets are conducted. The obtained results confirm the efficiency of our unsupervised feature selection scheme.