Robust multi-label feature learning-based dual space

Abstract

AbstractMulti-label learning handles instances associated with multiple class labels. The original label space is a logical matrix with entries from the Boolean domain $$\in \left\{ 0,1 \right\} $$ ∈ 0 , 1 . Logical labels cannot show the relative importance of each semantic label to the instances. Most existing methods map the input features to the label space using linear projections considering the label dependencies using a logical label matrix. However, the discriminative features are learned using one-way projection from the feature representation of an instance into a logical label space. There is no manifold in the learning space of logical labels, which limits the potential of learned models. We propose a novel method in multi-label learning to learn the projection matrix from the feature space to the semantic label space and project it back to the original feature space using encoder–decoder deep learning architecture. The key intuition which guides our method is that the discriminative features are identified due to mapping the features back and forth using two linear projections. To the best of our knowledge, this is one of the first attempts to study the ability to reconstruct the original features from the label manifold in multi-label learning. We show that the learned projection matrix identifies a subset of discriminative features across multiple semantic labels. Extensive experiments on real-world datasets show the superiority of the proposed method.