论文标题
通过近端梯度迭代制定用于源分离的Beurling Lasso
Formulating Beurling LASSO for Source Separation via Proximal Gradient Iteration
论文作者
论文摘要
Beurling Lasso概括了Lasso问题，以通过其总变化正规化有限的ra。尽管具有理论上的吸引力，但这个空间很难参数化，这构成了算法挑战。我们提出了一种用Beurling Lasso进行连续卷积源分离的表述，该套件避免了衡量标准的明确计算，而是采用了近端映射的二元性转换。
Beurling LASSO generalizes the LASSO problem to finite Radon measures regularized via their total variation. Despite its theoretical appeal, this space is hard to parametrize, which poses an algorithmic challenge. We propose a formulation of continuous convolutional source separation with Beurling LASSO that avoids the explicit computation of the measures and instead employs the duality transform of the proximal mapping.
