Weight-based label-unknown multi-view data set generation approach

摘要

Multi-view learning aims to solve multi-view data set which consists of multiple instances with different views. Traditional multi-view learning approaches always encounter small-scale label-known multi-view instances problem and insufficient discriminant information problem. Although some label-unknown multi-view data set generation approaches are developed to enhance useful discriminant information, the weights of views and features are not considered. This paper proposes a weight-based label-unknown multi-view data set generation approach (WLM) to overcome such a disadvantage. The procedure of WLM consists of three main steps. First, get the weights of views and features. Second, get similar instances of each label-known instance. Third, generate and select feasible label-unknown instances which are applied to multi-view learning approaches along with the original label-known multi-view instances. Further, comparisons and analysis about classification performance, clustering performance, bipartite ranking performance, image retrieval performance, significance on some multi-view data sets with some multi-view learning approaches validate the usefulness of WLM. (C) 2019 Elsevier B.V. All rights reserved.