论文标题
压缩分类的确定性桥梁回归
Deterministic Bridge Regression for Compressive Classification
论文作者
论文摘要
带有紧凑表示的模式分类是机器智能中的重要组成部分。在这项工作中，提出了一种分析桥解决方案进行压缩分类。该提案是基于使用近似$ \ ell_p $ -norm解决惩罚错误公式的。该解决方案具有针对过度确定的系统的原始形式，并以双重确定系统的双重形式提供。虽然原始形式适用于具有大数据样本的低维问题，但双重形式适用于高维问题，但具有少量的数据样本。该解决方案也已扩展到多个分类输出的问题。基于模拟和现实世界数据的数值研究验证了所提出的解决方案的有效性。
Pattern classification with compact representation is an important component in machine intelligence. In this work, an analytic bridge solution is proposed for compressive classification. The proposal has been based upon solving a penalized error formulation utilizing an approximated $\ell_p$-norm. The solution comes in a primal form for over-determined systems and in a dual form for under-determined systems. While the primal form is suitable for problems of low dimension with large data samples, the dual form is suitable for problems of high dimension but with a small number of data samples. The solution has also been extended for problems with multiple classification outputs. Numerical studies based on simulated and real-world data validated the effectiveness of the proposed solution.
