Consistency-exclusivity guided unsupervised multi-view feature selection

Unsupervised multi-view feature selection (UMFS) is an effective dimension reduction for multi-view data. It aims to obtain the important feature subset from multi-view data, which can significantly minimize the impacts of noises, outliers and redundancy. Although previous UMFS methods achieve remarkable achievements, they fail to fully take into account the consistency or the exclusivity hidden in multi-view data. To address this, this article presents a consistency-exclusivity guided unsupervised multi-view feature selection (CEUMFS) method. Specifically, we design a multi-view matrix factorization model to simultaneously explore the consistency and exclusivity in multi-view data. Meanwhile, we employ the Hilbert-Schmidt independence criterion (HSIC) to preserve the exclusivity of different views. Furthermore, we impose a nuclear norm on the consistent representation matrix to explore the consistency across views. At last, promising experimental results demonstrate the superiority of the proposed method compared with some state-of-the-art methods.