Fine-grained local label correlation for multi-label classification

Abstract

Comprehensive learning label correlation is conducive to boosting the accuracy of multi-label classification. While existing methods focus on exploring the correlation-aware original features or latent subspaces, they often overlook the role of correlation in deducing local structures. The oversight can result in suboptimal topic-based label correlation estimation and thus incur information loss. In contrast to the conventional single-granularity-based learning for local label correlation, we propose a multi-granularity correlation-based feature augmentation (MGOFA) model. MGOFA consists of three components that progressively refine the granularity of label correlation: granular-based feature augmentation for relative neighborhood-based class tendency estimation, granular-based latent topic mining for tendency-aware topic modeling, and fine-grained label correlation mining for augmented local label correlation learning. The information on neighborhood-based similarity between instances is explicitly leveraged and contributes to the model two-fold. Firstly, it induces the prototypes of latent topics, which share more knowledge with the label association. Secondly, it refines the discriminative granularity of the model by integrating it with the original features. Such a formulation simulates the viewpoint of human decision-making by automatically determining optimal solutions on both data and knowledge from coarse and refined granularity, respectively. Extensive comparisons completed of ten benchmarks demonstrate that MGOFA outperforms the state-of-the-art methods with satisfying convergence and sensitivity.