提出新方法分离多模态数据中的共享与特有特征，提升表征质量。
IndiSeek learns information-guided disentangled representations
- 通过独立性约束与重构损失联合优化，平衡特征独立与完整。
- 在合成数据和真实单细胞多组学数据上均优于现有方法。
- 适合需解耦共享与模态特有信息的研究场景。
解耦表征学习是多模态学习的核心任务。在单细胞多组学等现代应用中，共享特征与模态特有特征对刻画细胞状态及支持下游分析均至关重要。理想情况下，模态特有特征应与共享特征独立，同时保留各模态内全部互补信息。这一权衡可通过信息论准则表达，但基于互信息的目标难以可靠估计，其变分近似在实践中表现不佳。本文提出IndiSeek，一种新型解耦表征学习方法，结合独立性强制目标与计算高效的重构损失，该损失可界定向条件互信息。此形式显式平衡独立性与完整性，实现模态特有特征的合理提取。我们在合成模拟、CITE-seq数据集及多个真实多模态基准上验证了IndiSeek的有效性。
原文摘要 · Abstract (English)
Learning disentangled representations is a fundamental task in multi-modal learning. In modern applications such as single-cell multi-omics, both shared and modality-specific features are critical for characterizing cell states and supporting downstream analyses. Ideally, modality-specific features should be independent of shared ones while also capturing all complementary information within each modality. This tradeoff is naturally expressed through information-theoretic criteria, but mutual-information-based objectives are difficult to estimate reliably, and their variational surrogates often underperform in practice. In this paper, we introduce IndiSeek, a novel disentangled representation learning approach that addresses this challenge by combining an independence-enforcing objective with a computationally efficient reconstruction loss that bounds conditional mutual information. This formulation explicitly balances independence and completeness, enabling principled extraction of modality-specific features. We demonstrate the effectiveness of IndiSeek on synthetic simulations, a CITE-seq dataset and multiple real-world multi-modal benchmarks.
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