  Multi-label learning handles instances associated with multiple class labels.
The original label space is a logical matrix with entries from the Boolean
domain $\in \left \{ 0,1 \right \}$. Logical labels are not able to show the
relative importance of each semantic label to the instances. The vast majority
of existing methods map the input features to the label space using linear
projections with taking into consideration the label dependencies using 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. Given that there is no manifold in the learning space of logical labels,
which limits the potential of learned models. In this work, inspired from a
real-world example in image annotation to reconstruct an image from the label
importance and feature weights. We propose a novel method in multi-label
learning to learn the projection matrix from the feature space to semantic
label space and projects 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 map 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.
