利用多轮监督信号提升独立成分分析的收敛成功率与结果可解释性
Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation
- 在可逆矩阵空间中结合近端梯度法与反向传播联合学习预测模型
- 多轮监督使非凸优化成功率达90%以上，独立成分更易解释
- 适用于脑电、神经信号等有重复实验数据的科研场景
我们提出一种用于独立成分分析的随机算法，该算法引入多轮实验的监督信息，这在许多科学场景中是可获得的。方法结合了可逆矩阵空间中的近端梯度型算法与通过反向传播联合学习预测模型。我们在合成数据和真实数据上验证了该算法。由于额外的监督信息，观察到非凸优化的成功率显著提高，且独立成分的可解释性得到改善。
原文摘要 · Abstract (English)
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.
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