EMMS: Evidential Multi-Label Multi-Dimensional Selection

Abstract

Multi-label data often contain high-dimensional features, outlier instances, and noisy labels, all of which can lead to the curse of dimensionality and decreased performance in downstream tasks. Although numerous data reduction methods have been developed, existing approaches face two major limitations: 1) existing methods typically select features, instances, or labels independently, without considering how noise or redundancy in one dimension may negatively influence the selection of others; 2) there are very few feature and instance co-selection methods that commonly assume label annotations are free of noise, which is seldom true in practice. To address these issues, we propose Evidential Multi-Label Multi-Dimensional Selection (EMMS), which jointly performs feature, instance, and label selection on multi-label data. EMMS introduces a dual projection mechanism with sparsity constraints that transforms high-dimensional data first into a latent space and then into the label space. Simultaneously, projection residuals are explicitly modeled to facilitate the identification of representative instances, enabling unified selection across features, instances, and labels. Moreover, EMMS employs evidence theory to fuse instance-level and label-level evidence, thereby enhancing the reliability of the learned labels and reducing the influence of noisy labels, which in turn promotes multi-dimensional selection. Extensive experiments demonstrate that EMMS consistently outperforms state-of-the-art methods.