| Summary: | 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.
|
|---|
