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arXiv · 2507.09091

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA

Shayan K. Azmoodeh·Krishna Subramani·Paris Smaragdis
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Abstract

We generalize the low-rank decomposition problem, such as principal and independent component analysis (PCA, ICA) for continuous-time vector-valued signals and provide a model-agnostic implicit neural signal representation framework to learn numerical approximations to solve the problem. Modeling signals as continuous-time stochastic processes, we unify the approaches to both the PCA and ICA problems in the continuous setting through a contrast function term in the network loss, enforcing the desired statistical properties of the source signals (decorrelation, independence) learned in the decomposition. This extension to a continuous domain allows the application of such decompositions to point clouds and irregularly sampled signals where standard techniques are not applicable.

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Shayan K. Azmoodeh, Krishna Subramani, Paris Smaragdis. 2025-07-12. Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA. https://arxiv.org/abs/2507.09091

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