多用途套索
Multipurpose Lasso
摘要：
目前，l1惩罚似然由于其简单性和良好的理论性质而受到了广泛的关注。该方法被认为是一种可靠的方法，以便应用于包括高维情况在内的广泛应用中。另一方面，$L_1$驱动的方法，确切地说，套索相关的正则化，遭受稀疏性的损失时，观察的数量太低。在本文中，我们解决了一个新的可微近似的套索，可以产生相同的结果，套索和岭，也可以产生光滑的结果。我们证明了该模型的理论性质以及它的计算复杂性。由于可微性，所提出的方法可以通过文献中的大多数凸优化方法来实现。这意味着在真实系数接近于零的情况下具有更高的精度，这是LARS的主要问题。仿真研究和方法的灵活性表明，所提出的方法是一样可靠的玻璃和脊，可以在这两种情况下使用。
英文摘要：
Nowadays, l1 penalized likelihood has absorbed a high amount of consideration due to its simplicity and well developed theoretical properties. This method is known as a reliable method in order to apply in a broad range of applications including high-dimensional cases. On the other hand, $L_1$ driven methods, precisely lasso dependent regularizations, suffer the loss of sparsity when the number of observations is too low. In this paper we address a new differentiable approximation of lasso that can produce the same results as lasso and ridge and also can produce smooth results. We prove the theoretical properties of the model as well as its computation complexity. Due to differentiability, proposed method can be implemented by means of the majority of convex optimization methods in literature. That means a higher accuracy in situations where true coefficients are close to zero that is a major issue of LARS. Simulation study as well as flexibility of the method show that the proposed approach is as reliable as lass and ridge and can be used in both situations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
