Distribution-based Label Space Transformation for Multi-label Learning

TL;DR

This paper introduces a distribution-based label space transformation method for multi-label learning that preserves label correlations and addresses sparsity, leading to improved classification performance and efficiency.

Contribution

The proposed DLST model effectively captures label correlations and mitigates sparsity issues by using distribution similarity and KL-divergence, outperforming existing methods.

Findings

DLST achieves higher classification accuracy on benchmark datasets.

DLST is more computationally efficient than previous label space transformation methods.

DLST effectively handles label and data sparsity in multi-label learning.

Abstract

Multi-label learning problems have manifested themselves in various machine learning applications. The key to successful multi-label learning algorithms lies in the exploration of inter-label correlations, which usually incur great computational cost. Another notable factor in multi-label learning is that the label vectors are usually extremely sparse, especially when the candidate label vocabulary is very large and only a few instances are assigned to each category. Recently, a label space transformation (LST) framework has been proposed targeting these challenges. However, current methods based on LST usually suffer from information loss in the label space dimension reduction process and fail to address the sparsity problem effectively. In this paper, we propose a distribution-based label space transformation (DLST) model. By defining the distribution based on the similarity of label vectors, a more comprehensive label structure can be captured. Then, by minimizing KL-divergence of two distributions, the information of the original label space can be approximately preserved in the latent space. Consequently, multi-label classifier trained using the dense latent codes yields better performance. The leverage of distribution enables DLST to fill out additional information about the label correlations. This endows DLST the capability to handle label set sparsity and training data sparsity in multi-label learning problems. With the optimal latent code, a kernel logistic regression function is learned for the mapping from feature space to the latent space. Then ML-KNN is employed to recover the original label vector from the transformed latent code. Extensive experiments on several benchmark datasets demonstrate that DLST not only achieves high classification performance but also is computationally more efficient.