用信息论方法提取双序列共享结构，提升可解释性与稳定性。
InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis
- 基于信息论目标，分离共享与特异潜在表示
- 在真实和合成数据上表现优于传统动态CCA
- 适合需要可解释性建模的医学影像等序列分析
从高维序列数据中提取有意义的潜在表示是机器学习中的关键挑战，广泛应用于自然科学与工程领域。本文提出InfoDPCCA，一种动态概率典型相关分析框架，用于建模两个相互依赖的观测序列。该方法采用新颖的信息论目标函数，提取能捕捉两数据流间互结构的共享潜在表示，同时在表征压缩与预测充分性之间取得平衡，并学习各自序列特有的独立潜在成分。相比以往动态CCA模型（如DPCCA），本方法显式约束共享潜空间仅编码序列间的互信息，显著提升模型可解释性与鲁棒性。我们还设计了两步训练策略，弥合信息论表示学习与生成建模之间的差距，并引入残差连接机制增强训练稳定性。在合成数据和医学fMRI数据上的实验表明，InfoDPCCA在表示学习任务中表现优异。代码已开源：https://github.com/marcusstang/InfoDPCCA。
原文摘要 · Abstract (English)
Extracting meaningful latent representations from high-dimensional sequential data is a crucial challenge in machine learning, with applications spanning natural science and engineering. We introduce InfoDPCCA, a dynamic probabilistic Canonical Correlation Analysis (CCA) framework designed to model two interdependent sequences of observations. InfoDPCCA leverages a novel information-theoretic objective to extract a shared latent representation that captures the mutual structure between the data streams and balances representation compression and predictive sufficiency while also learning separate latent components that encode information specific to each sequence. Unlike prior dynamic CCA models, such as DPCCA, our approach explicitly enforces the shared latent space to encode only the mutual information between the sequences, improving interpretability and robustness. We further introduce a two-step training scheme to bridge the gap between information-theoretic representation learning and generative modeling, along with a residual connection mechanism to enhance training stability. Through experiments on synthetic and medical fMRI data, we demonstrate that InfoDPCCA excels as a tool for representation learning. Code of InfoDPCCA is available at https://github.com/marcusstang/InfoDPCCA.
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