提出新方法从多通道信号中分离振荡模式，更准更快更可解释。
Latent Mode Decomposition
- 用潜变量建模共享振荡成分，融合稀疏编码与模式分解
- 在合成与真实数据上优于现有方法，提升精度与效率
- 适合脑电、心电等多通道生理信号分析的科研人员
我们提出变分潜变量模式分解（VLMD），一种从多变量信号中提取振荡模式及关联结构的新算法。该方法解决了现有多变量模式分解（MMD）技术存在的计算成本高、参数敏感、通道间依赖建模弱等关键问题。其性能提升源于新的潜在模式分解（LMD）模型，该模型结合稀疏编码与模式分解，将多通道信号表示为共享潜变量成分的稀疏线性组合，每个成分由调幅-调频（AM-FM）振荡模式构成。这一设定使VLMD可在低维潜空间运行，增强对噪声的鲁棒性、可扩展性与可解释性。算法通过求解一个联合约束变分优化问题，同时保证重构保真度、稀疏性与频率正则化。在合成与真实数据集上的实验表明，VLMD在提取结构的准确性、效率与可解释性方面均优于当前最先进的MMD方法。
原文摘要 · Abstract (English)
We introduce Variational Latent Mode Decomposition (VLMD), a new algorithm for extracting oscillatory modes and associated connectivity structures from multivariate signals. VLMD addresses key limitations of existing Multivariate Mode Decomposition (MMD) techniques -including high computational cost, sensitivity to parameter choices, and weak modeling of interchannel dependencies. Its improved performance is driven by a novel underlying model, Latent Mode Decomposition (LMD), which blends sparse coding and mode decomposition to represent multichannel signals as sparse linear combinations of shared latent components composed of AM-FM oscillatory modes. This formulation enables VLMD to operate in a lower-dimensional latent space, enhancing robustness to noise, scalability, and interpretability. The algorithm solves a constrained variational optimization problem that jointly enforces reconstruction fidelity, sparsity, and frequency regularization. Experiments on synthetic and real-world datasets demonstrate that VLMD outperforms state-of-the-art MMD methods in accuracy, efficiency, and interpretability of extracted structures.
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