2012
NIPS
NeurIPS 2012
Nonconvex Penalization Using Laplace Exponents and Concave Conjugates
Abstract
In this paper we study sparsity-inducing nonconvex penalty functions using L´evy processes. We define such a penalty as the Laplace exponent of a subordina- tor. Accordingly, we propose a novel approach for the construction of sparsity- inducing nonconvex penalties. Particularly, we show that the nonconvex logarith- mic (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) dis- tance functions. Furthermore, the relationship between these two penalties is due to asymmetricity of the KL distance.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Mathematics & Optimization
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Trend Setter
— Probabilistic Modeling
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Keyword Pioneer
— nonconvex optimization
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— Artificial Intelligence, Computer Vision, Deep Learning, Machine Learning, Mathematics & Optimization, Reinforcement Learning
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Hot Topic Early Bird
— nonconvex optimization
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Mathematics & Optimization > Mathematics > Probability
Mathematics & Optimization > Optimization > Continuous Optimization
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Mathematics & Optimization > Probability > Stochastic Processes
Machine Learning > Optimization & Theory > Sparse Optimization
Machine Learning > Core Methods > Sparse Coding