Multi-view weak-label learning based on matrix completion

Abstract

Weak-label learning is an important branch of multi-label learning; it deals with samples annotated with incomplete (weak) labels. Previous work on weak-label learning mainly considers data represented by a single view. An intuitive way to leverage multiple features obtained from different views is to concatenate the features into a single vector. However, this process is not only prone to over-fitting and often results in very high time-complexity, but also ignores the potentially useful complementary information spread across the different views. In this paper, we propose an approach based on Matrix Completion for multi-view Weak-label Learning (McWL). Matrix completion (MC) has sound theoretical properties and is robust to missing values in both feature and label spaces. Our method enforces the optimization of multiple view integration and of MC-based classification within a unified objective function. Specifically, a kernel target alignment technique and the loss function of an MC-based classifier are used to jointly and iteratively adjust the weights assigned to individual views, and to optimize the classifier. McWL can selectively integrate views and is able to assign small weights to views of low quality. Extensive experiments on a broad range of datasets validate the effectiveness of our approach against competitive algorithms.