Robust supervised multi-view feature selection with weighted shared loss and maximum margin criterion

Supervised multi-view feature selection has recently been proven to be a valid way for reducing the dimensionality of data. This paper proposes a robust supervised multi-view feature selection method based on weighted shared loss and maximum margin criterion. Specifically, we fuse all weighted views to establish a shared loss term to maintain the complementary information of views, where the weight of each view can be automatically adjusted according to its contribution to the final task. Moreover, we adopt the capped l(2)-norm rather than the widely used l(2)-norm to measure the loss and remove the potential outliers. Further, we introduce the maximum margin criterion (MMC) into the feature selection and design a weighted view-based MMC such that it is a proper regularization associated with the proposed model. In this way, the inter-class and intra-class structure information of data is well exploited, which makes the margins of the samples belonging to the different classes increase and the margins of the samples belonging the same class decrease. Then the discriminative features of all views can be selected. The corresponding problem can be solved by the alternating direction method of multipliers (ADMM), which can realize the view-block calculation and reduce the computational complexity. Comprehensive experiments on various benchmark datasets and the application on the hydraulic system confirm the validity of our method. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a robust supervised multi-view feature selection method based on weighted shared loss and maximum margin criterion, which effectively exploits the information structure of the data and selects discriminative features by combining weighted views and introducing the maximum margin criterion.