Sparse Recovery for Protein Mass Spectrometry Data

This chapter contains sections titled: 1 Formulation as a Sparse Recovery Problem, 2 Adapting Sparse Recovery Methods to Non-Negativity and Heteroscedasticity, 3 A Pure Fitting Approach and its Advantages, 4 Systematic and Random Error, 5 Summary, Notes, References

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Bibliographic Details
Published inPractical Applications of Sparse Modeling
Format Book Chapter
LanguageEnglish
Published United States MIT Press 2014
The MIT Press
SeriesNeural Information Processing series
Subjects
Computer Science
Machine learning
Machine Learning & Neural Networks
Neuroscience
Online AccessGet full text
ISBN0262027720
9780262027724
DOI10.7551/mitpress/9333.003.0006

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Summary:This chapter contains sections titled: 1 Formulation as a Sparse Recovery Problem, 2 Adapting Sparse Recovery Methods to Non-Negativity and Heteroscedasticity, 3 A Pure Fitting Approach and its Advantages, 4 Systematic and Random Error, 5 Summary, Notes, References
ISBN:0262027720
9780262027724
DOI:10.7551/mitpress/9333.003.0006