Independent component analysis provides a principled framework for unsupervised representation learning, with solid theory on the identifiability of the latent code that generated the data, given only observations of mixtures thereof. Unfortunately, when the mixing is nonlinear, the model is provably nonidentifiable, since statistical independence alone does not sufficiently constrain the problem. Identifiability can be recovered in settings where additional, typically observed variables are included in the generative process. We investigate an alternative path and consider instead including assumptions reflecting the principle of independent causal mechanisms exploited in the field of causality. Specifically, our approach is motivated by thinking of each source as independently influencing the mixing process. This gives rise to a framework which we term independent mechanism analysis. We provide theoretical and empirical evidence that our approach circumvents a number of nonidentifiability issues arising in nonlinear blind source separation.

翻译：独立组成部分分析为未经监督的代表性学习提供了一个原则性框架,其中含有关于生成数据的潜在代码的可识别性的扎实理论,仅对其中的混合物进行观察。不幸的是,当混合为非线性时,该模型是无法辨认的,因为单靠统计独立不足以遏制问题。在将其他通常观察到的变量纳入基因变异过程的情况下,可以找到可识别性。我们调查了一条替代路径,考虑纳入反映因果领域所利用的独立因果机制原则的假设。具体地说,我们的方法的动机是将每个来源视为独立影响混合过程。这产生了一个我们称之为独立机制分析的框架。我们提供了理论和经验证据,证明我们的方法绕过了非线性源分离中产生的一些不可识别性问题。