论文标题
通过筛选测试及其应用加速稀疏的贝叶斯学习
Accelerated Sparse Bayesian Learning via Screening Test and Its Applications
论文作者
论文摘要
在高维设置中，稀疏结构对于记忆和计算复杂性的效率至关重要。对于线性系统，要查找具有过度完整字典的最稀少解决方案，通常是NP-硬的，因此应考虑替代性近似方法。在本文中，我们选择的替代方法是稀疏的贝叶斯学习，正如经验贝叶斯方法一样，它在鼓励溶液中的稀疏之前使用了参数化，而不是使用固定先验的其他方法（例如lasso）。但是，筛选测试旨在快速识别一组特征的子集，该功能在最佳解决方案中保证其系数为零，然后可以安全地从完整的字典中删除，以获得更小，更容易解决的问题。接下来，我们解决了较小的问题，之后可以通过用零填充较小的解决方案来恢复原始问题的解决方案。提出的方法的性能将在各种数据集和应用程序上进行检查。
In high-dimensional settings, sparse structures are critical for efficiency in term of memory and computation complexity. For a linear system, to find the sparsest solution provided with an over-complete dictionary of features directly is typically NP-hard, and thus alternative approximate methods should be considered. In this paper, our choice for alternative method is sparse Bayesian learning, which, as empirical Bayesian approaches, uses a parameterized prior to encourage sparsity in solution, rather than the other methods with fixed priors such as LASSO. Screening test, however, aims at quickly identifying a subset of features whose coefficients are guaranteed to be zero in the optimal solution, and then can be safely removed from the complete dictionary to obtain a smaller, more easily solved problem. Next, we solve the smaller problem, after which the solution of the original problem can be recovered by padding the smaller solution with zeros. The performance of the proposed method will be examined on various data sets and applications.
