Original paper
Compressive sensing based for mass spectrometry reconstruction
Published: Jul 1, 2016
Abstract
In this paper, we propose an efficient technique for dimensionality reduction of Mass Spectrometry (MS) data by employing Compressive Sensing (CS). Not only can CS significantly reduce MS data dimensionality, but it also will allow for full reconstruction of original data. The framework developed in this work is based on forming Sparse Difference (SD) to sparsify MS signals and implementing the Block Sparse Bayesian Learning (BSBL) to...
Paper Details
Title
Compressive sensing based for mass spectrometry reconstruction
Published Date
Jul 1, 2016
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History