Blind source separation (BSS) algorithms are unsupervised methods, which are the cornerstone of hyperspectral data analysis by allowing for physically meaningful data decompositions. BSS problems being ill-posed, the resolution requires efficient regularization schemes to better distinguish between the sources and yield interpretable solutions. For that purpose, we investigate a semi-supervised source separation approach in which we combine a projected alternating least-square algorithm with a learning-based regularization scheme. In this article, we focus on constraining the mixing matrix to belong to a learned manifold by making use of generative models. Altogether, we show that this allows for an innovative BSS algorithm, with improved accuracy, which provides physically interpretable solutions. The proposed method, coined sGMCA, is tested on realistic hyperspectral astrophysical data in challenging scenarios involving strong noise, highly correlated spectra and unbalanced sources. The results highlight the significant benefit of the learned prior to reduce the leakages between the sources, which allows an overall better disentanglement.
翻译:盲源分离算法是不受监督的方法,它是超光谱数据分析的基石,允许具有物理意义的数据分解。 BSS问题处理不当,决议要求有效的规范化计划,以更好地区分源和产生可解释的解决办法。为此,我们调查半监督的源分离法,在这种方法中,我们将预测的交替最小方位算法与基于学习的规范化计划结合起来。在本条中,我们侧重于限制混合矩阵,使其与通过使用基因模型而学到的元体相适应。我们共同表明,这允许采用创新的BSS算法,提高准确性,提供物理解释的解决方案。在涉及强烈噪音、高度关联的光谱和不平衡源的富有挑战性的情况下,对拟议的方法,即硬质的SGMCA,用现实的超光谱天物理数据进行测试。结果突出表明,在减少源间渗漏之前所学到的显著好处,这可以使总体的分解更好。