资源论文Nonconvex Penalization Using Laplace Exponents and Concave Conjugates

Nonconvex Penalization Using Laplace Exponents and Concave Conjugates

2020-01-13 | |  52 |   42 |   0

Abstract

In this paper we study sparsity-inducing nonconvex penalty functions using Le?vy processes. We define such a penalty as the Laplace exponent of a subordinator. Accordingly, we propose a novel approach for the construction of sparsityinducing nonconvex penalties. Particularly, we show that the nonconvex logarithmic (LOG) and exponential (EXP) penalty functions are the Laplace exponents of Gamma and compound Poisson subordinators, respectively. Additionally, we explore the concave conjugate of nonconvex penalties. We find that the LOG and EXP penalties are the concave conjugates of negative Kullback-Leiber (KL) distance functions. Furthermore, the relationship between these two penalties is due to asymmetricity of the KL distance.

上一篇:The Bethe Partition Function of Log-supermodular Graphical Models

下一篇:Selecting Diverse Features via Spectral Regularization

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...