Compressive sensing of jointly sparse signals as a method for dimensionality reduction of mass spectrometry data

Mass spectrometry (MS) is a technique that is applied in chemical and biomedical applications for molecular analysis. MS data has extremely high dimensionality that can be represented as a three dimension(3D) dataset. In this paper, we exploit 3D data structure and propose an effective model of compressive sensing (CS) for dimensionality reduction of MS data. A large set of MS data can be reduced significantly with high quality of data recovery....