An Improved LVSL with View Consensus

In multi-view multi-label learning (MVML), each sample can be represented by multiple view features and associated with multiple labels. Most existing MVML algorithms are based on the assumption that all views share the same label set. However, in practice, different views may contain distinct label information, that means a single view cannot fully represent all labels. Based on this issue, the LVSL algorithm effectively learns view-specific labels and obtains superior classification performance. However, LVSL still has the limitation that it fails to consider the correlations between views, leading to suboptimal learning results. In this paper, we propose an improved LVSL algorithm named LVSL_VC (LVSL with view consensus). We incorporate view consensus learning into the original LVSL framework. Firstly, we employ view weights to model view consensus, assuming that views with similar weights will yield similar prediction outputs, conversely, they will be different. Secondly, we integrate the view consensus into the LVSL framework and construct a new classification model. Finally, we utilize an alternating optimization method to solve the problem. Extensive experimental results demonstrate that the LVSL_VC outperforms other state-of-the-art MVML algorithms.