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.