论文标题
深层概率规范相关分析的变化推断
Variational Inference for Deep Probabilistic Canonical Correlation Analysis
论文作者
论文摘要
在本文中，我们提出了一个深层概率的多视图模型，该模型由基于概率的规范相关分析（CCA）的线性多视图层组成。该网络旨在将所有视图的变化分解为共享的潜在表示形式和一组特定视图的组件，其中共享的潜在表示旨在描述视图之间常见的潜在变化来源。开发了一个有效的变分推理过程，该过程近似于概率CCA的解决方案，近似于潜在概率多视图层的后验分布。还提出了对具有任意观点数量的模型的概括。实证研究证实，所提出的深层生成多视图模型可以成功地扩展到深层的变异推理到多视图学习，同时它有效地整合了多种观点之间的关系以减轻学习难度。
In this paper, we propose a deep probabilistic multi-view model that is composed of a linear multi-view layer based on probabilistic canonical correlation analysis (CCA) description in the latent space together with deep generative networks as observation models. The network is designed to decompose the variations of all views into a shared latent representation and a set of view-specific components where the shared latent representation is intended to describe the common underlying sources of variation among the views. An efficient variational inference procedure is developed that approximates the posterior distributions of the latent probabilistic multi-view layer while taking into account the solution of probabilistic CCA. A generalization to models with arbitrary number of views is also proposed. The empirical studies confirm that the proposed deep generative multi-view model can successfully extend deep variational inference to multi-view learning while it efficiently integrates the relationship between multiple views to alleviate the difficulty of learning.
