Multi-view Unsupervised Feature Selection via Consensus Guided Low-rank Tensor Learning

Lianzhi Wang, Cheng Liang, Wenjiao Dong, Wenlan Chen

Abstract: Recently, with the exponentially increased amount of multi-view data in various fields such as Multimedia and bioinformatics, multi-view unsupervised feature selection has attracted much attention due to its necessity in dealing with high-dimensional features. Although previous approaches have achieved great success, they generally ignore the consistent information and the high-order connections among views. In this paper, we present a general multi-view unsupervised feature selection model which integrates the common graph learning and feature selection into a unified framework. Specifically, our approach first learns a pseudo label matrix for each view by preserving the local data structure, and then stack them into a third-order tensor with low-rank constraint to explore the high-order connections among the views. In order to exploit the consistent information among different views, we seek a consensus graph matrix with optimal cluster structure by taking advantage of the view-specific pseudo label matrices and the rank constraint. Meanwhile, we adopt the sparse regression model to select discriminative features under the guidance of the final pseudo labels obtained from the learned consensus graph. We introduce an alternate optimization algorithm ground on the alternating direction method of multipliers (ADMM) to optimize the presented method. Extensive experiments on both machine learning and single-cell multi-omics datasets prove the effectiveness of our method. Moreover, the case study carried out on an ovarian cancer dataset further confirms the applicability of our method in identifying non-redundant and representative features.