# Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation

## TL;DR

This paper introduces a supervised stochastic gradient algorithm for multi-trial source separation, enhancing success rates and interpretability by integrating supervision and joint learning in ICA.

## Contribution

It presents a novel supervised stochastic gradient method for ICA that combines proximal gradient updates with backpropagation for joint learning.

## Key findings

- Increased success rate of non-convex optimization due to supervision
- Improved interpretability of independent components
- Validated on synthetic and real data experiments

## 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.