Multi-label classification is the challenging task of predicting the presence
and absence of multiple targets, involving representation learning and label
correlation modeling. We propose a novel framework for multi-label
classification, Multivariate Probit Variational AutoEncoder (MPVAE), that
effectively learns latent embedding spaces as well as label correlations. MPVAE
learns and aligns two probabilistic embedding spaces for labels and features
respectively. The decoder of MPVAE takes in the samples from the embedding
spaces and models the joint distribution of output targets under a Multivariate
Probit model by learning a shared covariance matrix. We show that MPVAE
outperforms the existing state-of-the-art methods on a variety of application
domains, using public real-world datasets. MPVAE is further shown to remain
robust under noisy settings. Lastly, we demonstrate the interpretability of the
learned covariance by a case study on a bird observation dataset.

本篇论文提出了一个用于多标签分类的新型框架，即多元 Probit 变分自编码器（MPVAE），该框架通过有效地学习潜在的嵌入空间和标签之间的相关性来实现。同时，MPVAE 在公开真实世界数据集上优于现有的最先进方法，并且进一步证明了在嘈杂环境下的稳健性。最后，通过鸟类观测数据集的案例研究，展示了学习共享协方差矩阵的可解释性。