Multiview Multilabel Classification With Group-Based Feature and Label Selection

Abstract

In real-world applications, data are often represented by multiple feature views and associated with multiple labels. In multiview learning, as different views may have noisy and irrelevant features, many works target multiview feature selection, but most of them only perform global feature selection, which means that all samples share the same feature selection weights. In addition, there are many studies in multilabel learning that assume all samples share the same label correlation. However, data may exhibit local patterns, such as feature selection weights and label correlations are locally shared by samples. To address this issue, in this paper, we propose a novel group-based model with local feature and label selection. The proposed model can project samples into different groups by performing group-based feature selection with each view having its own importance for grouping, where each group has its own related labels. The proposed model can then predict the semantics of samples by performing group-based label selection with each group having its own weight for prediction. Besides, the inter-group correlation is also mined and introduced in the above group-based learning process to ensure effective multilabel classification. Empirical studies on multiple image benchmarks validate the effectiveness of the proposed group-based model.