Learning Discriminative Features using Multi-label Dual Space

TL;DR

This paper introduces a novel multi-label learning method that employs a dual space projection with deep learning to identify discriminative features and reconstruct original data from label importance, surpassing existing approaches.

Contribution

It proposes a dual space projection approach with encoder-decoder architecture to learn discriminative features and reconstruct data, addressing limitations of one-way label projections.

Findings

Outperforms existing multi-label learning methods on real-world datasets.

Effectively identifies discriminative features across multiple labels.

Demonstrates the ability to reconstruct original features from label space.

Abstract

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.