Multi-label data are widely encountered in fields such as image recognition, text classification, and bioinformatics. Unlike traditional single-label data, each instance in a multi-label setting is typically associated with multiple labels. For example, a travel photo may be annotated with labels such as “beach”, “sunset” and “people”. However, real-world multi-label data often involve high dimensionality, outliers, and label noise. These issues can easily result in the curse of dimensionality and interfere with a model’s ability to learn from informative samples and reliable label information, thereby impairing the performance of downstream tasks such as classification and prediction.
To address the above problems, conventional methods usually select representative features, samples, or labels independently, while overlooking the interdependencies among them during the selection process. Moreover, these methods often assume that label annotations are noise-free, an assumption that is rarely valid in practical applications. To overcome these limitations, this paper proposes an evidence-theory-based multi-dimensional selection method for multi-label data, which jointly selects features, samples, and labels. Specifically, the proposed method employs a dual-projection framework with sparse constraints to map high-dimensional data first into a latent space and then into the label space. By explicitly modeling projection residuals, it identifies representative samples and thereby enables the joint selection of features, samples, and labels. Furthermore, evidence theory is introduced to integrate information from both the sample level and the label level, thereby improving the reliability of label learning and mitigating the adverse effects of noisy labels.
Extensive experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods across multiple evaluation metrics. Case studies on multi-label image datasets further confirm its effectiveness.
