论文标题
从多视图数据中的变异解释性学习
Variational Interpretable Learning from Multi-view Data
论文作者
论文摘要
规范相关分析（CCA）的主要思想是将不同的视图映射到具有最大相关性的常见潜在空间。我们提出了用于多视图学习的深度可解释的典型相关分析（DICCA）。开发的模型通过使用深层生成网络将现有的线性CCA现有潜在变量模型扩展到非线性模型。 DICCA旨在解开多视图数据的共享和特定于视图的变化。为了进一步使模型更容易解释，我们将稀疏性诱导的先验放在潜在的重量上，该重量由由视图特异性发电机组成的结构化变异自动编码器。现实世界数据集的经验结果表明，我们的方法在范围内具有竞争力。
The main idea of canonical correlation analysis (CCA) is to map different views onto a common latent space with maximum correlation. We propose a deep interpretable variational canonical correlation analysis (DICCA) for multi-view learning. The developed model extends the existing latent variable model for linear CCA to nonlinear models through the use of deep generative networks. DICCA is designed to disentangle both the shared and view-specific variations for multi-view data. To further make the model more interpretable, we place a sparsity-inducing prior on the latent weight with a structured variational autoencoder that is comprised of view-specific generators. Empirical results on real-world datasets show that our methods are competitive across domains.
