Compressed signal processing (CSP) is a branch of compressive sensing (CS), which gives a direction to solve a class of signal processing problems directly from the compressive measurements of a signal. CSP utilizes the information preserved in the compressive measurements of a signal to solve certain inference problems like: classification, detection, and estimation, without reconstructing the original signal. It further simplifies the signal processing compared to conventional CS by omitting their complex reconstruction stage. This, in turn, reduces the implementation complexity of signal processing systems. This paper investigates the performance of CSP for classification application. After extracting the features from compressive measurements, these features or the data instances are used for classification purpose. Through experimental analysis, it has been found that as the CS undersampling factor is increased, the overlapping among the data instances predominates. This results in a complex decision boundary, which in turn degrades the classification accuracy at higher undersampling factors. To overcome the above issue, this paper proposes the use of a machine learning method known as overlap aware learning along with CSP. This generates a smoother decision boundary and hence improves the classification accuracy at higher undersampling factors. The simulation results show the trend of improved classification accuracy using the proposed method. An analysis of the proposed method has been done on different datasets and based on run-time complexity and complexity vs gain analysis to verify the effectiveness of proposed method.

中文翻译：

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重叠感知压缩信号分类


压缩信号处理（CSP）是压缩感知（CS）的一个分支，它为直接从信号的压缩测量中解决一类信号处理问题提供了方向。 CSP 利用信号压缩测量中保留的信息来解决某些推理问题，例如分类、检测和估计，而无需重建原始信号。与传统 CS 相比，它省略了复杂的重建阶段，进一步简化了信号处理。这反过来又降低了信号处理系统的实现复杂性。本文研究了 CSP 在分类应用中的性能。从压缩测量中提取特征后，这些特征或数据实例用于分类目的。通过实验分析发现，随着CS欠采样因子的增加，数据实例之间的重叠占主导地位。这会导致复杂的决策边界，进而降低较高欠采样因子下的分类精度。为了克服上述问题，本文提出将一种称为重叠感知学习的机器学习方法与 CSP 一起使用。这会生成更平滑的决策边界，从而提高较高欠采样因子下的分类精度。仿真结果显示了使用所提出的方法提高分类精度的趋势。我们在不同的数据集上对所提出的方法进行了分析，并基于运行时复杂性和复杂性与增益分析来验证所提出方法的有效性。