Latent Mode Decomposition

• URL: http://arxiv.org/abs/2505.17797v1
• Date: Fri, 23 May 2025 12:16:35 GMT
• Title: Latent Mode Decomposition
• Authors: Manuel Morante, Naveed ur Rehman,
• Abstract summary: We introduce Variational Latent Mode Decomposition (VLMD), a new algorithm for extracting oscillatory modes from multivariate signals.<n>Its improved performance is driven by a novel underlying model, Latent Mode Decomposition (LMD), which blends sparse coding and mode decomposition.<n> Experiments on synthetic and real-world datasets demonstrate that VLMD outperforms state-of-the-art MMD methods in accuracy, efficiency, and interpretability.
• Score: 0.3683202928838613
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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