Multiple graph unsupervised feature selection

Xingzhong Du, Yan Yan, Pingbo Pan, Guodong Long, Lei Zhao

Abstract: Highlights • A novel unsupervised feature selection algorithm is proposed which combines multiple graphs to uncover the manifold. • The l 2 , p norm has more flexibility in controlling the sparse learning, thereby resulting in better performance. • Combining multiple graph and l 2 , p norm results in better performance. Abstract Feature selection improves the quality of the model by filtering out the noisy or redundant part. In the unsupervised scenarios, the selection is challenging due to the unavailability of the labels. To overcome that, the graphs which can unfold the geometry structure on the manifold are usually used to regularize the selection process. These graphs can be constructed either in the local view or the global view. As the local graph is more discriminative, previous methods tended to use the local graph rather than the global graph. But the global graph also has useful information. In light of this, in this paper, we propose a multiple graph unsupervised feature selection method to leverage the information from both local and global graphs. Besides that, we enforce the l 2 , p norm to achieve more flexible sparse learning. The experiments which inspect the effects of multiple graph and l 2 , p norm are conducted respectively on various datasets, and the comparisons to other mainstream methods are also presented in this paper. The results support that the multiple graph could be better than the single graph in the unsupervised feature selection, and the overall performance of the proposed method is higher than the other comparisons.