Nonconvex Penalization Using Laplace Exponents and Concave Conjugates

Zhang, Zhihua, Tu, Bojun

Neural Information Processing Systems 

In this paper we study sparsity-inducing nonconvex penalty functions using Lévy processes. We define such a penalty as the Laplace exponent of a subordinator. Accordingly,we propose a novel approach for the construction of sparsityinducing nonconvexpenalties. 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.

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