用结构化非线性ICA从噪声数据中分离可识别特征
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
摘要：
我们介绍了一个新的一般识别框架的原则性解开称为结构化非线性独立成分分析（SNICA）。我们的贡献是将深度生成模型的可识别性理论扩展到非常广泛的一类结构化模型。虽然以前的工作已经证明了特定类别的时间序列模型的可识别性，我们的定理将其扩展到更一般的时间结构以及具有更复杂结构（如空间依赖性）的模型。特别是，我们建立的主要结果，该框架的可识别性，即使在存在未知分布的噪声。最后，作为我们的框架的灵活性的一个例子，我们介绍了第一个非线性ICA模型的时间序列，结合了以下非常有用的属性：它占非平稳性和自相关在一个完全无监督的设置;进行降维;隐藏状态模型;并使原则的估计和推理变分最大似然。
英文摘要：
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA). Our contribution is to extend the identifiability theory of deep generative models for a very broad class of structured models. While previous works have shown identifiability for specific classes of time-series models, our theorems extend this to more general temporal structures as well as to models with more complex structures such as spatial dependencies. In particular, we establish the major result that identifiability for this framework holds even in the presence of noise of unknown distribution. Finally, as an example of our framework's flexibility, we introduce the first nonlinear ICA model for time-series that combines the following very useful properties: it accounts for both nonstationarity and autocorrelation in a fully unsupervised setting; performs dimensionality reduction; models hidden states; and enables principled estimation and inference by variational maximum-likelihood.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
