提出无需先验的自足独立成分分析，让信号能自主恢复缺失信息。
Self-sufficient Independent Component Analysis via KL Minimizing Flows
- 通过最小化条件KL散度实现信号自足性建模
- 无需先验或似然模型，避免传统方法的灵活性限制
- 迭代优化解混流，稳定且不依赖对抗训练
我们研究非线性独立成分分析（ICA）中从数据中学习解耦信号的问题。受自监督学习启发，提出学习自足信号：恢复出的信号应能仅凭其余分量重建自身缺失部分，而无需依赖其他信号。该问题被形式化为条件KL散度最小化。相比传统最大似然估计，该算法无需对原始信号施加先验或观测模型，从而摆脱了对模型灵活性的限制。为解决KL散度最小化问题，提出一种序列算法，在每次迭代中降低散度并学习最优解混流模型。该方法完全避免了常见的不稳定对抗训练。在合成与真实数据集上的实验验证了方法的有效性。
原文摘要 · Abstract (English)
We study the problem of learning disentangled signals from data using non-linear Independent Component Analysis (ICA). Motivated by advances in self-supervised learning, we propose to learn self-sufficient signals: A recovered signal should be able to reconstruct a missing value of its own from all remaining components without relying on any other signals. We formulate this problem as the minimization of a conditional KL divergence. Compared to traditional maximum likelihood estimation, our algorithm is prior-free and likelihood-free, meaning that we do not need to impose any prior on the original signals or any observational model, which often restricts the model's flexibility. To tackle the KL divergence minimization problem, we propose a sequential algorithm that reduces the KL divergence and learns an optimal de-mixing flow model at each iteration. This approach completely avoids the unstable adversarial training, a common issue in minimizing the KL divergence. Experiments on toy and real-world datasets show the effectiveness of our method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。