Hi List, I have a conceptual question about compressed sensing, and it has nothing to do with Python (yet!).
So I don't know if it is appropriate to ask this question on this mailing list. Please excuse me. Suppose I have an array X with N dimensions, and after a linear transformation I get an array X_hat which is S-sparse, where S<<N. Is this condition sufficient to recover the signal through random (enough) sampling? What mathematical relations should apply on S and N to ensure sparse recovery? How randomly should I sample the transformed signal X_hat to ensure a good recovery? Thanks ------------------------------------------------------------------------------ Keep Your Developer Skills Current with LearnDevNow! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-d2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
