Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification

TL;DR

The paper introduces J-Play, a linearized subspace learning method that jointly and progressively learns discriminative features for multi-label classification, improving explainability, generalization, and cost-effectiveness.

Contribution

It proposes a novel joint and progressive linear subspace learning technique specifically designed for multi-label classification tasks.

Findings

J-Play outperforms state-of-the-art methods in experiments.

The method effectively learns high-level semantic features.

It improves classification accuracy and interpretability.

Abstract

Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.