论文标题
通过协方差多元概率模型，基于分解的变异自动编码器的多标签分类
Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model
论文作者
论文摘要
多标签分类是预测有多个目标的存在和不存在的具有挑战性的任务，涉及表示和标签相关建模。我们提出了一个用于多标签分类的新型框架，即多元概率变异自动编码器（MPVAE），该框架有效地学习了潜在的嵌入空间以及标签相关性。 MPVAE分别学习和对齐两个概率嵌入空间的标签和特征。 MPVAE的解码器通过学习共享协方差矩阵，从嵌入空间中吸收样品，并模拟多元概率模型下输出目标的联合分布。我们表明，使用公共现实世界数据集，MPVAE的表现优于各种应用程序域上现有的最新方法。在嘈杂的环境下，进一步证明MPVAE保持健壮。最后，我们通过鸟类观察数据集的案例研究证明了学识渊博的协方差。
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.
