Multi-label classification is an important research topic in machine learning, for which exploiting label dependencies is an effective modeling principle. Recently, probabilistic models have shown great potential in discovering dependencies among labels. In this paper, motivated by the recent success of multi-view learning to improve the generalization performance, we propose a novel multi-view probabilistic model named latent conditional Bernoulli mixture (LCBM) for multi-label classification. LCBM is a generative model taking features from different views as inputs, and conditional on the latent subspace shared by the views a Bernoulli mixture model is adopted to build label dependencies. Inside each component of the mixture, the labels have a weak correlation which facilitates computational convenience. The mean field variational inference framework is used to carry out approximate posterior inference in the probabilistic model, where we propose a Gaussian mixture variational autoencoder (GMVAE) for effective posterior approximation. We further develop a scalable stochastic training algorithm for efficiently optimizing the model parameters and variational parameters, and derive an efficient prediction procedure based on greedy search. Experimental results on multiple benchmark datasets show that our approach outperforms other state-of-the-art methods under various metrics.

中文翻译：

---

LCBM：多标签分类的多视图概率模型

多标签分类是机器学习中的一个重要研究课题，利用标签依赖是一种有效的建模原则。最近，概率模型在发现标签之间的依赖关系方面显示出巨大的潜力。在本文中，受多视图学习最近成功提高泛化性能的启发，我们提出了一种新的多视图概率模型，称为潜在条件伯努利混合（LCBM），用于多标签分类。LCBM 是一种生成模型，以来自不同视图的特征作为输入，并以视图共享的潜在子空间为条件，采用伯努利混合模型来构建标签依赖关系。在混合物的每个成分内部，标签具有弱相关性，这有助于计算方便。平均场变分推理框架用于在概率模型中进行近似后验推理，我们提出了一种高斯混合变分自编码器 (GMVAE) 以实现有效的后验近似。我们进一步开发了一种可扩展的随机训练算法，用于有效优化模型参数和变分参数，并基于贪婪搜索推导出有效的预测程序。在多个基准数据集上的实验结果表明，我们的方法在各种指标下都优于其他最先进的方法。我们进一步开发了一种可扩展的随机训练算法，用于有效优化模型参数和变分参数，并基于贪婪搜索推导出有效的预测程序。在多个基准数据集上的实验结果表明，我们的方法在各种指标下都优于其他最先进的方法。我们进一步开发了一种可扩展的随机训练算法，用于有效优化模型参数和变分参数，并基于贪婪搜索推导出有效的预测程序。在多个基准数据集上的实验结果表明，我们的方法在各种指标下都优于其他最先进的方法。