Semi-supervised multi-label learning with missing labels by exploiting feature-label correlations

The majority of multi-learning techniques now in use presuppose that there will be enough labeled instances. But in real-world applications, it is frequently the case that only partial labels are included for each training instance. This is either because getting a fully labeled training set takes a lot of time and effort or because doing so is expensive. Multi-label learning with missing labels, on the other hand, has greater practical value. In this paper, we propose a brand-new semi-supervised multi-label learning method (SMLMFC) that specifically addresses missing-label scenarios. After successfully filling in the missing labels for instances using two-stage label correlations, SMLMFC trains a semi-supervised multi-label classifier by imposing feature-label correlation restrictions directly on the output of labels. The complex relationships between features and labels can be learned and implicitly captured through feature-label correlations, in particular. The experimental results on a number of real-world multi-label datasets confirm that SMLMFC has strong competitiveness in comparison to other state-of-the-art methods.