  The redundant features existing in high dimensional datasets always affect
the performance of learning and mining algorithms. How to detect and remove
them is an important research topic in machine learning and data mining
research. In this paper, we propose a graph based approach to find and remove
those redundant features automatically for high dimensional data. Based on the
sparse learning based unsupervised feature selection framework, Sparse Feature
Graph (SFG) is introduced not only to model the redundancy between two
features, but also to disclose the group redundancy between two groups of
features. With SFG, we can divide the whole features into different groups, and
improve the intrinsic structure of data by removing detected redundant
features. With accurate data structure, quality indicator vectors can be
obtained to improve the learning performance of existing unsupervised feature
selection algorithms such as multi-cluster feature selection (MCFS). Our
experimental results on benchmark datasets show that the proposed SFG and
feature redundancy remove algorithm can improve the performance of unsupervised
feature selection algorithms consistently.
