Work

Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation

Year: 2025

Type: preprint

Abstract: We develop a stochastic algorithm for independent component analysis that incorporates multi-trial supervision, which is available in many scientific contexts. The method blends a proximal gradient-type algorithm in the space of invertible matrices with joint learning of a prediction model through backpropagation. We illustrate the proposed algorithm on synthetic and real data experiments. In particular, owing to the additional supervision, we observe an increased success rate of the non-convex optimization and the improved interpretability of the independent components.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2508.20618

Authors: Mehta, Ronak, Otto, Mateus Piovezan, Stanis, Noah, Yazdan-Shahmorad, Azadeh, Harchaoui, Zaid

Language: English

Topic: Blind Source Separation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4414449031

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