鲁棒稀疏性的非凸p范数投影

Mithun Das Gupta, Sanjeev Kumar
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引用次数: 12

摘要

本文研究了投影框架内Lp范数(p≤1)的性质。从非线性优化问题的KKT方程出发,利用其关键性质得到了非负单纯形上Lp范数投影的算法。我们比较了L1投影,它需要真实范数的先验知识,以及最近压缩感知文献中提出的基于硬阈值的稀疏化。我们展示了这些技术在不同视觉应用中的性能改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-convex P-Norm Projection for Robust Sparsity
In this paper, we investigate the properties of Lp norm (p ≤1) within a projection framework. We start with the KKT equations of the non-linear optimization problem and then use its key properties to arrive at an algorithm for Lp norm projection on the non-negative simplex. We compare with L1 projection which needs prior knowledge of the true norm, as well as hard thresholding based sparsification proposed in recent compressed sensing literature. We show performance improvements compared to these techniques across different vision applications.
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